introduction speech for recognition

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

80+ Rewards and Recognition Speech Examples for Inspiration

Discover impactful rewards and recognition speech example. Inspire your team with words of appreciation. Elevate your recognition game today!

Jan 25th 2024 • 26 min read

In today's competitive corporate landscape, where employee motivation and engagement are crucial for success, rewards and recognition speech examples have emerged as powerful tools to inspire and appreciate the efforts of employees. Whether it's to celebrate milestones, acknowledge outstanding performance, or simply boost morale, a well-crafted rewards and recognition speech can leave a lasting impact on the recipients.

If you're searching for the perfect blend of words to uplift and motivate your team, look no further. In this blog, we will delve into the art of rewards and recognition speeches, exploring examples that encapsulate the essence of appreciation and inspire employees to reach new heights of success.

Whether you're a team leader, manager, or someone looking to express your appreciation to a colleague, our blog will provide you with a treasure trove of rewards and recognition speech examples that are sure to captivate and inspire. So, grab a cup of coffee, sit back, and let us guide you through the world of appreciation and recognition in the workplace.

What Is A Rewards and Recognition Speech?

A rewards and recognition speech is a formal address given to acknowledge and appreciate individuals or groups for their exceptional achievements or contributions. It serves as a platform to publicly recognize the efforts and accomplishments of deserving individuals , boosting morale, and fostering a positive work culture. This type of speech is commonly delivered during award ceremonies, employee appreciation events, or annual gatherings where appreciation and recognition are key objectives.

A well-crafted rewards and recognition speech celebrates the recipients' accomplishments, highlights their impact on the organization, and inspires others to strive for similar success. In essence, it is an opportunity to acknowledge, motivate, and express gratitude towards individuals who have made a significant difference in their field or organization.

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How Rewards and Recognition Impact Employee Motivation and Engagement

Employee motivation and engagement are crucial factors in determining the success of a company. One effective way to enhance motivation and engagement is through rewards and recognition. By acknowledging and appreciating employees' efforts and accomplishments, organizations can create a positive work environment that encourages productivity and fosters loyalty. We will explore how rewards and recognition can impact employee motivation and engagement.

1. Increased Job Satisfaction

Rewarding and recognizing employees for their hard work not only boosts their confidence but also increases their overall job satisfaction. When employees feel valued and appreciated, they are more likely to enjoy their work and feel a sense of fulfillment in their roles . This satisfaction translates into higher motivation and engagement, as employees are more committed to their tasks and strive to exceed expectations.

2. Improved Performance

Rewards and recognition serve as powerful motivators that drive employees to perform at their best. When employees know that their efforts will be acknowledged and rewarded, they are more likely to go the extra mile and demonstrate exceptional performance. As a result, organizations witness improved productivity, increased efficiency, and higher quality outputs. By recognizing and rewarding outstanding performance, companies can create a culture of excellence and continuous improvement.

3. Enhanced Employee Morale

Recognition plays a significant role in boosting employee morale. When employees receive acknowledgment for their achievements, it reinforces their belief in their capabilities and contributions. This positive reinforcement not only motivates employees to continue performing well but also creates a supportive and encouraging work environment. High employee morale leads to increased job satisfaction, lower turnover rates, and a stronger sense of belonging within the organization.

4. Strengthened Employee Engagement

Rewards and recognition contribute to higher levels of employee engagement. Engaged employees are those who are fully committed to their work and actively contribute to the success of the organization. When employees feel recognized and valued, they develop a stronger emotional connection to their work and the company's goals. This emotional investment drives their engagement, leading to increased productivity, creativity, and innovation.

5. Retention and Attraction of Talent

An effective rewards and recognition program can significantly impact employee retention and attraction. Recognized and rewarded employees are more likely to remain loyal to their organization and less likely to seek employment elsewhere. In addition, a positive work culture that emphasizes rewards and recognition becomes an attractive selling point for potential candidates. By showcasing a commitment to employee motivation and engagement, organizations can attract top talent, reduce turnover costs, and maintain a highly skilled workforce.

Rewards and recognition have a profound impact on employee motivation and engagement. By implementing a comprehensive program that appreciates and acknowledges employees' efforts, organizations can create a work environment that fosters satisfaction, productivity, and loyalty. Investing in rewards and recognition not only benefits individual employees but also contributes to the long-term success of the organization as a whole.

• Words Of Appreciation For Good Work Done By Team • How To Recognize Employees • Recognition Examples • How Do You Like To Be Recognized • Recognizing A Coworker • Reward And Recognition Ideas • Fun Employee Recognition Ideas • Formal Recognition • Team Member Recognition • Performance Recognition • Reasons To Recognize Employees • Reward And Recognition Strategies • Recognition For Leadership • How To Recognize Employees For A Job Well Done • Reasons For Rewarding Employees • Employee Wall Of Fame Ideas

1. Celebrating Team Milestones

Recognizing and rewarding the achievements of individual team members or the entire team when they reach significant milestones, such as completing a project, meeting a target, or reaching a certain number of sales.

2. Employee of the Month

Recognizing outstanding employees by selecting one as the Employee of the Month, based on their exceptional performance, dedication, and positive impact on the organization.

3. Sales Contest Winners

Acknowledging the top performers in sales contests and rewarding them with incentives, such as cash bonuses, gift cards, or extra vacation days.

4. Most Improved Employee

Recognizing employees who have shown significant improvement in their performance, skills, or productivity, and highlighting their dedication to personal growth and development.

5. Customer Service Heroes

Acknowledging employees who have gone above and beyond to provide exceptional customer service, resolving challenging situations, and ensuring customer satisfaction.

6. Leadership Excellence

Recognizing managers or team leaders who have demonstrated exceptional leadership skills, inspiring and motivating their team members to achieve outstanding results.

7. Innovation Champions

Celebrating employees who have introduced innovative ideas, processes, or solutions that have had a positive impact on the organization, encouraging a culture of creativity and continuous improvement.

8. Outstanding Team Player

Recognizing individuals who consistently contribute to the success of their team, displaying a collaborative mindset, and supporting their colleagues in achieving common goals.

9. Safety Initiatives

Acknowledging employees who have taken proactive measures to ensure a safe working environment, promoting safety protocols, and reducing accidents or injuries.

10. Excellence in Problem-Solving

Recognizing employees who have demonstrated exceptional problem-solving skills, showcasing their ability to analyze complex situations and find effective solutions.

11. Mentorship and Coaching

Celebrating individuals who have dedicated their time and expertise to mentor and coach their colleagues, supporting their professional growth and development.

12. Going the Extra Mile

Recognizing employees who consistently go above and beyond their regular duties, displaying exceptional commitment and dedication to their work.

13. Team Building Champions

Acknowledging individuals who have organized and led successful team-building activities, fostering a positive team spirit and enhancing collaboration within the organization.

14. Employee Wellness Advocates

Recognizing employees who have actively promoted and contributed to the well-being of their colleagues, encouraging a healthy work-life balance and creating a positive work environment.

15. Community Service

Celebrating employees who have actively participated in community service initiatives, volunteering their time and skills to make a positive impact on society.

16. Outstanding Project Management

Recognizing individuals who have demonstrated exceptional project management skills, successfully leading and delivering complex projects on time and within budget.

17. Customer Appreciation

Acknowledging employees who have received positive feedback or testimonials from customers, highlighting their exceptional service and dedication to customer satisfaction.

18. Quality Excellence

Recognizing employees who consistently deliver high-quality work, ensuring that the organization maintains its standards of excellence and customer satisfaction.

19. Team Spirit

Celebrating the unity and camaraderie within a team, acknowledging their strong bond and collaborative efforts in achieving shared goals.

20. Creativity and Innovation

Recognizing employees who have shown creativity and innovative thinking in their work, introducing new ideas, and driving positive change within the organization.

21. Initiative and Proactivity

Acknowledging employees who take the initiative and demonstrate a proactive approach to their work, identifying opportunities for improvement and taking action to implement them.

22. Cross-Functional Collaboration

Celebrating individuals who have successfully collaborated with colleagues from different departments or teams, fostering a culture of teamwork and achieving synergy in their projects.

23. Learning and Development Champions

Recognizing employees who have shown a commitment to their own learning and development, actively seeking opportunities to acquire new skills and knowledge.

24. Outstanding Customer Retention

Acknowledging employees who have played a crucial role in ensuring customer loyalty and retention, consistently delivering exceptional service and building strong relationships with customers.

25. Adaptability and Flexibility

Celebrating employees who have demonstrated adaptability and flexibility in their work, successfully navigating through change and embracing new challenges.

26. Continuous Improvement

Recognizing individuals who consistently seek ways to improve processes, systems, or workflows, contributing to the organization's overall efficiency and effectiveness.

27. Employee Engagement Advocates

Acknowledging employees who have actively promoted employee engagement initiatives, creating a positive and motivating work environment.

28. Exceptional Time Management

Recognizing employees who have demonstrated exceptional time management skills, effectively prioritizing tasks and meeting deadlines.

29. Resilience and Perseverance

Celebrating individuals who have shown resilience and perseverance in the face of challenges or setbacks, inspiring others to overcome obstacles and achieve success.

30. Teamwork in Crisis

Acknowledging the teamwork and collaboration displayed by employees during a crisis or challenging situation, highlighting their ability to work together under pressure.

31. Leadership in Diversity and Inclusion

Recognizing leaders who have actively promoted diversity and inclusion within the organization, fostering an inclusive and equitable work environment.

32. Outstanding Problem-Solving

Celebrating employees who consistently demonstrate exceptional problem-solving skills, showcasing their ability to analyze complex situations and find innovative solutions.

33. Excellence in Customer Retention

Recognizing employees who have played a crucial role in ensuring customer loyalty and satisfaction, consistently delivering exceptional service and building strong relationships.

34. Inspirational Leadership

Acknowledging leaders who have inspired and motivated their team members to achieve outstanding results, displaying exceptional leadership qualities.

35. Customer Service Excellence

Celebrating employees who consistently provide exceptional customer service, going above and beyond to meet customer needs and exceed expectations.

36. Collaboration and Teamwork

Recognizing individuals or teams who have demonstrated outstanding collaboration and teamwork, achieving common goals through effective communication and cooperation.

37. Employee Empowerment

Acknowledging employees who have actively empowered their colleagues, fostering a culture of autonomy, trust, and accountability within the organization.

38. Sales Achievement Awards

Celebrating top performers in sales, acknowledging their exceptional sales skills, and their contribution to the organization's growth and success.

39. Learning and Development Pioneers

Recognizing employees who have taken the initiative in their own learning and development, actively seeking opportunities to acquire new skills and knowledge.

40. Innovation and Creativity

Celebrating individuals who have introduced innovative ideas, processes, or solutions that have had a positive impact on the organization, encouraging a culture of creativity and continuous improvement.

41. Leadership in Crisis

Acknowledging leaders who have displayed exceptional leadership skills during a crisis or challenging situation, guiding their team members and making effective decisions under pressure.

42. Outstanding Customer Service

Recognizing employees who consistently provide exceptional customer service, demonstrating a commitment to customer satisfaction and building strong customer relationships.

43. Collaboration Across Departments

Celebrating individuals or teams who have successfully collaborated with colleagues from different departments, fostering cross-functional synergy and achieving shared goals.

44. Employee Growth and Development

Acknowledging employees who have shown dedication to their own growth and development, actively seeking opportunities to enhance their skills and knowledge.

45. Quality Excellence

46. resilience and adaptability.

Celebrating individuals who have demonstrated resilience and adaptability in the face of challenges or change, inspiring others to overcome obstacles and embrace new opportunities.

47. Leadership in Employee Engagement

Acknowledging leaders who have actively promoted employee engagement initiatives, creating a positive and motivating work environment.

48. Outstanding Problem-Solving

Recognizing employees who consistently demonstrate exceptional problem-solving skills, showcasing their ability to analyze complex situations and find innovative solutions.

49. Customer Appreciation

Celebrating employees who have received positive feedback or testimonials from customers, highlighting their exceptional service and commitment to customer satisfaction.

50. Teamwork in Crisis

51. leadership in diversity and inclusion, 52. inspirational leadership.

Celebrating leaders who have inspired and motivated their team members to achieve outstanding results, displaying exceptional leadership qualities.

53. Exceptional Time Management

Acknowledging employees who have demonstrated exceptional time management skills, effectively prioritizing tasks and meeting deadlines.

54. Continuous Improvement

55. employee empowerment.

Celebrating employees who have actively empowered their colleagues, fostering a culture of autonomy, trust, and accountability within the organization.

56. Sales Achievement Awards

Recognizing top performers in sales, acknowledging their exceptional sales skills, and their contribution to the organization's growth and success.

57. Learning and Development Pioneers

Celebrating employees who have taken the initiative in their own learning and development, actively seeking opportunities to acquire new skills and knowledge.

58. Innovation and Creativity

Acknowledging individuals who have introduced innovative ideas, processes, or solutions that have had a positive impact on the organization, encouraging a culture of creativity and continuous improvement.

59. Leadership in Crisis

Recognizing leaders who have displayed exceptional leadership skills during a crisis or challenging situation, guiding their team members and making effective decisions under pressure.

60. Outstanding Customer Service

Celebrating employees who consistently provide exceptional customer service, demonstrating a commitment to customer satisfaction and building strong customer relationships.

61. Collaboration Across Departments

Recognizing individuals or teams who have successfully collaborated with colleagues from different departments, fostering cross-functional synergy and achieving shared goals.

62. Employee Growth and Development

Celebrating employees who have shown dedication to their own growth and development, actively seeking opportunities to enhance their skills and knowledge.

63. Quality Excellence

Acknowledging employees who consistently deliver high-quality work, ensuring that the organization maintains its standards of excellence and customer satisfaction.

64. Resilience and Adaptability

Recognizing individuals who have demonstrated resilience and adaptability in the face of challenges or change, inspiring others to overcome obstacles and embrace new opportunities.

65. Leadership in Employee Engagement

Celebrating leaders who have actively promoted employee engagement initiatives, creating a positive and motivating work environment.

66. Outstanding Problem-Solving

Acknowledging employees who consistently demonstrate exceptional problem-solving skills, showcasing their ability to analyze complex situations and find innovative solutions.

67. Customer Appreciation

Recognizing employees who have received positive feedback or testimonials from customers, highlighting their exceptional service and commitment to customer satisfaction.

68. Teamwork in Crisis

Celebrating the teamwork and collaboration displayed by employees during a crisis or challenging situation, highlighting their ability to work together under pressure.

69. Leadership in Diversity and Inclusion

Acknowledging leaders who have actively promoted diversity and inclusion within the organization, fostering an inclusive and equitable work environment.

70. Inspirational Leadership

Recognizing leaders who have inspired and motivated their team members to achieve outstanding results, displaying exceptional leadership qualities.

71. Exceptional Time Management

Celebrating employees who have demonstrated exceptional time management skills, effectively prioritizing tasks and meeting deadlines.

72. Continuous Improvement

Acknowledging individuals who consistently seek ways to improve processes, systems, or workflows, contributing to the organization's overall efficiency and effectiveness.

73. Employee Empowerment

Recognizing employees who have actively empowered their colleagues, fostering a culture of autonomy, trust, and accountability within the organization.

74. Sales Achievement Awards

75. learning and development pioneers.

Acknowledging employees who have taken the initiative in their own learning and development, actively seeking opportunities to acquire new skills and knowledge.

76. Innovation and Creativity

Recognizing individuals who have introduced innovative ideas, processes, or solutions that have had a positive impact on the organization, encouraging a culture of creativity and continuous improvement.

77. Leadership in Crisis

Celebrating leaders who have displayed exceptional leadership skills during a crisis or challenging situation, guiding their team members and making effective decisions under pressure.

78. Outstanding Customer Service

Acknowledging employees who consistently provide exceptional customer service, demonstrating a commitment to customer satisfaction and building strong customer relationships.

79. Collaboration Across Departments

80. employee growth and development, the importance of a rewards and recognition speech.

In the business world, rewards and recognition play a crucial role in motivating employees and fostering a positive company culture. While giving a gift with a note may be a thoughtful gesture, delivering a rewards and recognition speech adds a personal touch and amplifies the impact of the recognition. This is especially significant for major employee rewards, such as a 10-year anniversary or other significant recognition events.

1. Personal Connection and Appreciation

A rewards and recognition speech allows the business owner to personally connect with the employee and express gratitude for their dedication and achievements. By taking the time to deliver a speech, the business owner demonstrates that they genuinely value and appreciate the employee's contributions. This personal touch fosters a deeper sense of connection and appreciation within the company culture.

2. Public Acknowledgment and Inspiration

When a rewards and recognition speech is delivered in a public setting, such as a company-wide event or meeting, it not only acknowledges the efforts of the individual employee but also inspires and motivates others. Seeing their colleagues being recognized and appreciated encourages other employees to strive for excellence and contribute to the success of the company. It creates a positive competitive environment where employees are motivated to perform their best.

3. Reinforcement of Company Values

A rewards and recognition speech provides an opportunity for the business owner to reinforce the company's values and goals. By highlighting the employee's achievements and how they align with the company's mission, vision, and values, the speech emphasizes the importance of these core principles. This reinforcement helps to solidify a positive company culture that is built on shared values and a sense of purpose.

4. Celebration and Team Building

Delivering a rewards and recognition speech creates a celebratory atmosphere that brings employees together as a team. It showcases the collective achievements of the company and encourages a sense of camaraderie and unity. Celebrating accomplishments through a speech allows employees to feel proud of their individual and team successes, which further strengthens the bonds within the organization.

5. Emotional Connection and Employee Engagement

A rewards and recognition speech taps into the emotional aspect of recognition. It goes beyond a simple gift and note, as it allows the business owner to communicate genuine appreciation and admiration for the employee's contributions. This emotional connection enhances employee engagement and makes them feel valued and invested in the company's success. Engaged employees are more likely to be loyal, productive, and committed to the organization.

Delivering a rewards and recognition speech is a powerful way for business owners to show appreciation and reinforce a positive company culture. It establishes a personal connection, inspires others, reinforces company values, builds team spirit, and fosters employee engagement. By recognizing and celebrating employees through a speech, business owners can create a work environment that thrives on recognition, motivation, and a shared sense of purpose.

How To Implement A Successful Rewards and Recognition Program

Creating and implementing a rewards and recognition program in a company can have numerous benefits, such as increasing employee motivation, improving performance, and enhancing employee satisfaction. It is essential to approach the implementation strategically to ensure its effectiveness. Here are some effective strategies for implementing a successful rewards and recognition program:

1. Define Clear Objectives and Goals

Before designing your rewards and recognition program, it is crucial to define clear objectives and goals. What do you want to achieve with the program? Are you aiming to boost employee morale, increase productivity, or enhance teamwork? Clearly defining your objectives will help you tailor the program to meet specific needs and ensure that it aligns with the company's overall goals.

2. Involve Employees in the Process

To make your rewards and recognition program truly effective, involve employees in the process. Conduct surveys or focus groups to gather their input and preferences. By involving employees, you can ensure that the program resonates with them, making it more meaningful and valuable. Involving employees in the decision-making process can foster a sense of ownership and engagement.

3. Develop a Variety of Recognition Initiatives

To cater to the diverse needs and preferences of your employees, it is essential to develop a variety of recognition initiatives. Consider implementing both formal and informal recognition programs. Formal recognition may include annual awards ceremonies or performance-based bonuses, while informal recognition can involve small gestures like personalized thank-you notes or shout-outs during team meetings. By offering a range of initiatives, you can ensure that different types of accomplishments are acknowledged and valued.

4. Make the Program Transparent and Equitable

Transparency and equity are crucial in a rewards and recognition program. Clearly communicate the criteria for receiving recognition and the rewards associated with it. Ensure that the criteria are fair, consistent, and unbiased . This transparency will promote a sense of fairness and prevent any perception of favoritism or inequality within the organization.

5. Create a Culture of Appreciation

Implementing a rewards and recognition program is not enough; it must be supported by a culture of appreciation. Encourage managers and leaders to regularly acknowledge and appreciate their team members' efforts. Foster a work environment where recognition is not limited to the formal program but becomes a natural part of everyday interactions. This culture of appreciation will amplify the impact of the formal program and create a positive and motivating work atmosphere.

6. Evaluate and Refine

Continuous evaluation and refinement are essential for the long-term success of a rewards and recognition program. Regularly collect feedback from employees and managers to identify areas of improvement. Analyze the effectiveness of different initiatives and adjust them as necessary. By regularly evaluating and refining the program, you can ensure that it remains relevant, impactful, and aligned with the evolving needs of the organization.

Implementing a rewards and recognition program requires thoughtful planning and execution. By following these strategies, you can create a program that not only rewards and recognizes employees' contributions but also inspires and motivates them to achieve their best.

10 Reasons for Rewards and Recognition & How To Determine Who To Reward

1. boost employee morale.

Rewarding and recognizing employees for their hard work can significantly boost morale. It shows employees that their efforts are valued and appreciated, which in turn motivates them to continue performing at their best.

2. Improve Employee Engagement

When employees feel recognized and rewarded, they are more likely to be engaged in their work. Engaged employees are more productive, creative, and willing to go above and beyond to achieve company goals.

3. Increase Employee Retention

Recognizing and rewarding employees for their contributions can help increase employee retention. Employees who feel valued are more likely to stay with the company, reducing turnover rates and the associated costs of hiring and training new employees.

4. Foster a Positive Work Culture

Implementing a rewards and recognition program can help foster a positive work culture. When employees see their peers being acknowledged for their achievements, it creates a supportive and collaborative environment where everyone strives for success.

5. Reinforce Desired Behaviors

Rewards and recognition can be used to reinforce desired behaviors and values within the organization. By publicly acknowledging and rewarding employees who exemplify these behaviors, it encourages others to follow suit.

6. Encourage Continuous Improvement

Recognizing employees for their good work encourages a culture of continuous improvement. It motivates employees to seek out opportunities to enhance their skills and knowledge, leading to personal and professional growth .

7. Enhance Team Collaboration

Rewarding and recognizing the efforts of individuals within a team can strengthen team collaboration. It fosters a sense of camaraderie and encourages teamwork, as employees understand the importance of supporting one another to achieve common goals.

8. Increase Customer Satisfaction

When employees feel recognized and appreciated, they are more likely to provide excellent customer service. Happy and engaged employees create positive interactions with customers, leading to increased customer satisfaction and loyalty.

9. Drive Innovation

Rewards and recognition can also drive innovation within an organization. When employees are acknowledged for their innovative ideas or problem-solving skills, it encourages a culture of creativity and encourages others to think outside the box.

10. Attract Top Talent

A well-established rewards and recognition program can help attract top talent to the company. By showcasing the company's commitment to valuing and rewarding its employees, it becomes an attractive proposition for potential candidates.

How To Determine Who To Reward as a Business Owner

1. performance metrics.

Use performance metrics such as sales targets, customer satisfaction ratings, or project completion rates to identify employees who have consistently exceeded expectations.

2. Peer Feedback

Seek feedback from colleagues and team members to identify individuals who have made significant contributions to the team or have gone above and beyond their assigned duties.

3. Customer Feedback

Consider customer feedback when determining who to reward. Look for employees who have received positive feedback or have gone the extra mile to ensure customer satisfaction.

4. Quality of Work

Consider the quality of work produced by employees. Reward those who consistently deliver high-quality work and attention to detail.

5. Leadership and Initiative

Identify employees who display leadership qualities and take initiative in solving problems or improving processes. These individuals often have a positive impact on the team and deserve recognition.

6. Innovation and Creativity

Recognize employees who have demonstrated innovation and creativity in their work. These individuals contribute fresh ideas and solutions that drive the company forward.

7. Collaboration and Teamwork

Acknowledge employees who excel at collaboration and teamwork. These individuals build strong relationships with their colleagues and contribute to a positive and productive work environment.

8. Longevity and Seniority

Consider rewarding employees based on their longevity and seniority within the company. This recognizes their loyalty and commitment to the organization over the years.

9. Going Above and Beyond

Identify employees who consistently go above and beyond their job responsibilities. Reward those who have taken on additional tasks, volunteered for extra projects, or contributed to the company's success in exceptional ways.

10. Personal Development and Growth

Recognize employees who actively seek opportunities for personal development and growth. Reward those who have acquired new skills or certifications that benefit both themselves and the company.

By considering these factors, business owners can fairly determine who to reward and ensure that recognition is given to those who truly deserve it.

Potential Challenges To Avoid When Implementing A Rewards and Recognition Program

1. lack of clarity and consistency in criteria.

The success of a rewards and recognition program depends on clearly defined and consistent criteria for determining who is eligible for recognition and what types of rewards are available. Failing to establish and communicate these criteria can lead to confusion and dissatisfaction among employees . It is essential to ensure that the criteria are fair, transparent, and aligned with organizational goals.

2. Inadequate communication and feedback

Effective communication is crucial when implementing a rewards and recognition program. Employees need to understand the purpose of the program, how it works, and what is expected of them to be eligible for recognition. Regular feedback is also vital to ensure that employees understand why they are being recognized and to reinforce positive behaviors. Without proper communication and feedback, employees may feel undervalued or uncertain about the program's objectives.

3. Limited variety and personalization of rewards

Offering a limited range of rewards or failing to personalize them to individual preferences can diminish the impact of a rewards and recognition program. Different employees may value different types of rewards, whether it's financial incentives, professional development opportunities, or public recognition. It is important to consider individual preferences and offer a variety of rewards that align with employees' needs and aspirations.

4. Lack of alignment with organizational values

A rewards and recognition program should align with the core values and goals of an organization. If the program does not reflect the organization's values or reinforce behaviors that contribute to its success, it may be perceived as inauthentic or disconnected from the broader objectives. It is essential to design a program that supports the desired culture and drives employee engagement and performance in a way that aligns with the organization's mission and values.

5. Failure to recognize team efforts

While recognizing individual achievements is important, it is equally crucial to acknowledge and reward team accomplishments. Neglecting to recognize the contributions of teams can create a sense of competition and undermine collaboration, which are essential for overall organizational success. Incorporate team-based rewards and recognition initiatives to foster a sense of camaraderie and motivate collective efforts.

6. Inconsistent and infrequent recognition

Recognition should be timely and consistent to be effective. Delayed or infrequent recognition can diminish its impact and may lead to a decrease in employee motivation. Establish a regular cadence for recognition and ensure that it is provided promptly when deserved. Consistency in recognizing achievements will help reinforce positive behaviors and maintain employee engagement.

7. Lack of management support and involvement

The success of a rewards and recognition program relies heavily on the support and involvement of management. If leaders do not actively participate or demonstrate enthusiasm for the program, employees may perceive it as insignificant or insincere. It is crucial to engage managers at all levels and empower them to recognize and reward employees' achievements. Managers should serve as role models and champions of the program to foster a culture of appreciation and recognition.

Implementing a rewards and recognition program can be a powerful tool for motivating employees, increasing engagement, and driving organizational success. By addressing and avoiding these potential challenges and pitfalls, organizations can create a program that effectively recognizes and rewards employees for their contributions and accomplishments.

Best Practices for Implementing A Rewards and Recognition Program

Implementing a rewards and recognition program is a crucial step in fostering employee engagement, motivation, and loyalty within an organization. It requires careful planning and execution to ensure its effectiveness. We will explore the best practices for implementing a successful rewards and recognition program.

1. Clearly Define Program Objectives

Before implementing a rewards and recognition program, it is essential to define clear objectives. This involves identifying the behaviors, achievements, or contributions that will be rewarded, as well as the desired outcomes of the program. By clearly defining program objectives, organizations can align the program with their overall business goals and ensure its relevance and effectiveness.

2. Align Rewards with Employee Preferences

To ensure the success of a rewards and recognition program, it is important to align the rewards with the preferences and aspirations of employees. Conducting surveys or focus groups can help gather employee feedback and identify the types of rewards that would motivate and resonate with them the most. This could include monetary incentives, non-monetary rewards, or a combination of both.

3. Make the Recognition Timely and Specific

Recognition should be timely and specific to have a lasting impact on employee motivation and morale. It is important to recognize and reward employees promptly after they have achieved the desired behaviors or accomplishments. Recognition should be specific, highlighting the specific actions or contributions that led to the recognition. This helps reinforce desired behaviors and demonstrates the value placed on those actions.

4. Foster a Culture of Peer-to-Peer Recognition

In addition to formal recognition from managers or supervisors, organizations should encourage peer-to-peer recognition. This creates a positive and inclusive work environment where employees feel valued and appreciated by their colleagues. Implementing a platform or system for employees to easily recognize and appreciate each other's efforts can enhance teamwork, collaboration, and overall employee satisfaction.

5. Communicate and Promote the Program

Effective communication and promotion of the rewards and recognition program are essential for its success. Organizations should clearly communicate the program's objectives, eligibility criteria, and rewards to all employees. This can be done through email announcements, intranet postings, or even in-person meetings. Regular reminders and updates about the program can help maintain awareness and encourage participation.

6. Ensure Fairness and Transparency

A successful rewards and recognition program should be perceived as fair and transparent by employees. The criteria for eligibility and selection of recipients should be clearly communicated and consistently applied. To build trust and credibility, it is important to ensure that the program is free from favoritism or bias. Regular evaluations of the program's effectiveness and fairness can help identify any areas for improvement.

7. Measure and Track Results

To evaluate the effectiveness of a rewards and recognition program, it is important to measure and track its results. This can be done through employee surveys, performance metrics, or feedback sessions. By analyzing the data, organizations can identify any gaps or areas for improvement and make necessary adjustments to enhance the program's impact.

By following these best practices, organizations can implement a rewards and recognition program that effectively motivates and engages employees. This, in turn, leads to increased productivity, employee satisfaction, and overall organizational success. Implementing a well-designed program that aligns with the organization's goals and employee preferences is crucial for achieving these desired outcomes.

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Meaningful Connections Across the Globe

At its core, Giftpack's mission is to foster meaningful connections through the power of personalized gifting. By taking into account the recipient's individuality and preferences, Giftpack ensures that each gift is a reflection of thoughtfulness and care. Whether it's strengthening relationships with employees, delighting customers, or expressing gratitude to valued clients, Giftpack enables individuals and businesses to make a lasting impact on those who matter most.

In a world where personalization and meaningful connections are highly valued, Giftpack stands out as a trailblazer in revolutionizing the corporate gifting landscape. With their innovative approach, vast catalog of global gifts, user-friendly platform, and commitment to personalized experiences, Giftpack is transforming the way we think about rewards and recognition.

• Modern Employee Recognition Programs • Employee Award Programs • Recognizing Employee Contributions • Employee Recognition Program Best Practices • Rewards And Recognition System • How To Create An Employee Recognition Program

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Essential Guide to Automatic Speech Recognition Technology

Over the past decade, AI-powered speech recognition systems have slowly become part of our everyday lives, from voice search to virtual assistants in contact centers, cars, hospitals, and restaurants. These speech recognition developments are made possible by deep learning advancements.

Developers across many industries now use automatic speech recognition (ASR) to increase business productivity, application efficiency, and even digital accessibility. This post discusses ASR, how it works, use cases, advancements, and more.

What is automatic speech recognition?

Speech recognition technology is capable of converting spoken language (an audio signal) into written text that is often used as a command.

Today’s most advanced software can accurately process varying language dialects and accents. For example, ASR is commonly seen in user-facing applications such as virtual agents, live captioning, and clinical note-taking. Accurate speech transcription is essential for these use cases.

Developers in the speech AI space also use  alternative terminologies  to describe speech recognition such as ASR, speech-to-text (STT), and voice recognition.

ASR is a critical component of  speech AI , which is a suite of technologies designed to help humans converse with computers through voice.

Why natural language processing is used in speech recognition

Developers are often unclear about the role of natural language processing (NLP) models in the ASR pipeline. Aside from being applied in language models, NLP is also used to augment generated transcripts with punctuation and capitalization at the end of the ASR pipeline.

After the transcript is post-processed with NLP, the text is used for downstream language modeling tasks:

• Sentiment analysis
• Text analytics
• Text summarization
• Question answering

Speech recognition algorithms

Speech recognition algorithms can be implemented in a traditional way using statistical algorithms or by using deep learning techniques such as neural networks to convert speech into text.

Traditional ASR algorithms

Hidden Markov models (HMM) and dynamic time warping (DTW) are two such examples of traditional statistical techniques for performing speech recognition.

Using a set of transcribed audio samples, an HMM is trained to predict word sequences by varying the model parameters to maximize the likelihood of the observed audio sequence.

DTW is a dynamic programming algorithm that finds the best possible word sequence by calculating the distance between time series: one representing the unknown speech and others representing the known words.

Deep learning ASR algorithms

For the last few years, developers have been interested in deep learning for speech recognition because statistical algorithms are less accurate. In fact, deep learning algorithms work better at understanding dialects, accents, context, and multiple languages, and they transcribe accurately even in noisy environments.

Some of the most popular state-of-the-art speech recognition acoustic models are Quartznet , Citrinet , and Conformer . In a typical speech recognition pipeline, you can choose and switch any acoustic model that you want based on your use case and performance.

Implementation tools for deep learning models

Several tools are available for developing deep learning speech recognition models and pipelines, including Kaldi , Mozilla DeepSpeech, NVIDIA NeMo , NVIDIA Riva , NVIDIA TAO Toolkit , and services from Google, Amazon, and Microsoft.

Kaldi, DeepSpeech, and NeMo are open-source toolkits that help you build speech recognition models. TAO Toolkit and Riva are closed-source SDKs that help you develop customizable pipelines that can be deployed in production.

Cloud service providers like Google, AWS, and Microsoft offer generic services that you can easily plug and play with.

Deep learning speech recognition pipeline

An ASR pipeline consists of the following components:

• Spectrogram generator that converts raw audio to spectrograms.
• Acoustic model that takes the spectrograms as input and outputs a matrix of probabilities over characters over time.
• Decoder (optionally coupled with a language model) that generates possible sentences from the probability matrix.
• Punctuation and capitalization model that formats the generated text for easier human consumption.

A typical deep learning pipeline for speech recognition includes the following components:

• Data preprocessing
• Neural acoustic model
• Decoder (optionally coupled with an n-gram language model)
• Punctuation and capitalization model

Figure 1 shows an example of a deep learning speech recognition pipeline:.

Datasets are essential in any deep learning application. Neural networks function similarly to the human brain. The more data you use to teach the model, the more it learns. The same is true for the speech recognition pipeline.

A few popular speech recognition datasets are

• LibriSpeech
• Fisher English Training Speech
• Mozilla Common Voice (MCV)
• 2000 HUB 5 English Evaluation Speech
• AN4 (includes recordings of people spelling out addresses and names)
• Aishell-1/AIshell-2 Mandarin speech corpus

Data processing is the first step. It includes data preprocessing and augmentation techniques such as speed/time/noise/impulse perturbation and time stretch augmentation, fast Fourier Transformations (FFT) using windowing, and normalization techniques.

For example, in Figure 2, the mel spectrogram is generated from a raw audio waveform after applying FFT using the windowing technique.

We can also use perturbation techniques to augment the training dataset. Figures 3 and 4 represent techniques like noise perturbation and masking being used to increase the size of the training dataset in order to avoid problems like overfitting.

The output of the data preprocessing stage is a spectrogram/mel spectrogram, which is a visual representation of the strength of the audio signal over time. 

Mel spectrograms are then fed into the next stage: a neural acoustic model . QuartzNet, CitriNet, ContextNet, Conformer-CTC, and Conformer-Transducer are examples of cutting-edge neural acoustic models. Multiple ASR models exist for several reasons, such as the need for real-time performance, higher accuracy, memory size, and compute cost for your use case.

However, Conformer-based models are becoming more popular due to their improved accuracy and ability to comprehend. The acoustic model returns the probability of characters/words at each time stamp.

Figure 5 shows the output of the acoustic model, with time stamps. 

The acoustic model’s output is fed into the decoder along with the language model. Decoders include beam search and greedy decoders, and language models include n-gram language, KenLM, and neural scoring. When it comes to the decoder, it helps to generate top words, which are then passed to language models to predict the correct sentence.

In Figure 6, the decoder selects the next best word based on the probability score. Based on the final highest score, the correct word or sentence is selected and sent to the punctuation and capitalization model.

The ASR pipeline generates text with no punctuation or capitalization.

Finally, a punctuation and capitalization model is used to improve the text quality for better readability. Bidirectional Encoder Representations from Transformers (BERT) models are commonly used to generate punctuated text.

Figure 7 shows a simple example of a before-and-after punctuation and capitalization model.

Speech recognition industry impact

There are many unique applications for ASR . For example, speech recognition could help industries such as finance, telecommunications, and unified communications as a service (UCaaS) to improve customer experience, operational efficiency, and return on investment (ROI).

Speech recognition is applied in the finance industry for applications such as call center agent assist and trade floor transcripts. ASR is used to transcribe conversations between customers and call center agents or trade floor agents. The generated transcriptions can then be analyzed and used to provide real-time recommendations to agents. This adds to an 80% reduction in post-call time.

Furthermore, the generated transcripts are used for downstream tasks:

• Intent and entity recognition

Telecommunications

Contact centers are critical components of the telecommunications industry. With contact center technology, you can reimagine the telecommunications customer center, and speech recognition helps with that.

As previously discussed in the finance call center use case, ASR is used in Telecom contact centers to transcribe conversations between customers and contact center agents to analyze them and recommend call center agents in real time. T-Mobile uses ASR for quick customer resolution , for example.

Unified communications as a software

COVID-19 increased demand for UCaaS solutions, and vendors in the space began focusing on the use of speech AI technologies such as ASR to create more engaging meeting experiences.

For example, ASR can be used to generate live captions in video conferencing meetings. Captions generated can then be used for downstream tasks such as meeting summaries and identifying action items in notes.

Future of ASR technology

Speech recognition is not as easy as it sounds. Developing speech recognition is full of challenges, ranging from accuracy to customization for your use case to real-time performance. On the other hand, businesses and academic institutions are racing to overcome some of these challenges and advance the use of speech recognition capabilities.

ASR challenges

Some of the challenges in developing and deploying speech recognition pipelines in production include the following:

• Lack of tools and SDKs that offer state-of-the-art (SOTA) ASR models makes it difficult for developers to take advantage of the best speech recognition technology.
• Limited customization capabilities that enable developers to fine-tune on domain-specific and context-specific jargon, multiple languages, dialects, and accents to have your applications understand and speak like you
• Restricted deployment support; for example, depending on the use case, the software should be capable of being deployed in any cloud, on-premises, edge, and embedded. 
• Real-time speech recognition pipelines; for instance, in a call center agent assist use case, we cannot wait several seconds for conversations to be transcribed before using them to empower agents.

For more information about the major pain points that developers face when adding speech-to-text capabilities to applications, see Solving Automatic Speech Recognition Deployment Challenges .

ASR advancements

Numerous advancements in speech recognition are occurring on both the research and software development fronts. To begin, research has resulted in the development of several new cutting-edge ASR architectures, E2E speech recognition models, and self-supervised or unsupervised training techniques.

On the software side, there are a few tools that enable quick access to SOTA models, and then there are different sets of tools that enable the deployment of models as services in production. 

Key takeaways

Speech recognition continues to grow in adoption due to its advancements in deep learning-based algorithms that have made ASR as accurate as human recognition. Also, breakthroughs like multilingual ASR help companies make their apps available worldwide, and moving algorithms from cloud to on-device saves money, protects privacy, and speeds up inference.

NVIDIA offers Riva , a speech AI SDK, to address several of the challenges discussed above. With Riva, you can quickly access the latest SOTA research models tailored for production purposes. You can customize these models to your domain and use case, deploy on any cloud, on-premises, edge, or embedded, and run them in real-time for engaging natural interactions.

Learn how your organization can benefit from speech recognition skills with the free ebook, Building Speech AI Applications .

Related resources

• GTC session: Speech AI Demystified
• GTC session: Mastering Speech AI for Multilingual Multimedia Transformation
• GTC session: Human-Like AI Voices: Exploring the Evolution of Voice Technology
• NGC Containers: Domain Specific NeMo ASR Application
• NGC Containers: MATLAB
• Webinar: How Telcos Transform Customer Experiences with Conversational AI

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Introduction to Automatic Speech Recognition (ASR)

Speech Processing

16 minute read

Maël Fabien

co-founder & ceo @ biped.ai

• Switzerland
• Custom Social Profile Link

This article provides a summary of the course “Automatic speech recognition” by Gwénolé Lecorvé from the Research in Computer Science (SIF) master , to which I added notes of the Statistical Sequence Processing course of EPFL, and from some tutorials/personal notes. All references are presented at the end.

Introduction to ASR

What is asr.

Automatic Speech Recognition (ASR), or Speech-to-text (STT) is a field of study that aims to transform raw audio into a sequence of corresponding words.

Some of the speech-related tasks involve:

• speaker diarization: which speaker spoke when?
• speaker recognition: who spoke?
• spoken language understanding: what’s the meaning?
• sentiment analysis: how does the speaker feel?

The classical pipeline in an ASR-powered application involves the Speech-to-text, Natural Language Processing and Text-to-speech.

ASR is not easy since there are lots of variabilities:

• variability between speakers (inter-speaker)
• variability for the same speaker (intra-speaker)
• noise, reverberation in the room, environment…
• articulation
• elisions (grouping some words, not pronouncing them)
• words with similar pronounciation
• size of vocabulary
• word variations

From a Machine Learning perspective, ASR is also really hard:

• very high dimensional output space, and a complex sequence to sequence problem
• few annotated training data
• data is noisy

How is speech produced?

Let us first focus on how speech is produced. An excitation \(e\) is produced through lungs. It takes the form of an initial waveform, describes as an airflow over time.

Then, vibrations are produced by vocal cords, filters \(f\) are applied through pharynx, tongue…

The output signal produced can be written as \(s = f * e\), a convolution between the excitation and the filters. Hence, assuming \(f\) is linear and time-independent:

From the initial waveform, we generate the glotal spectrum, right out of the vocal cords. A bit higher the vocal tract, at the level of the pharynx, pitches are formed and produce the formants of the vocal tract. Finally, the output spectrum gives us the intensity over the range of frequencies produced.

Breaking down words

In automatic speech recognition, you do not train an Artificial Neural Network to make predictions on a set of 50’000 classes, each of them representing a word.

In fact, you take an input sequence, and produce an output sequence. And each word is represented as a phoneme , a set of elementary sounds in a language based on the International Phonetic Alphabet (IPA). To learn more about linguistics and phonetic, feel free to check this course from Harvard. There are around 40 to 50 different phonemes in English.

Phones are speech sounds defined by the acoustics, potentially unlimited in number,

For example, the word “French” is written under IPA as : / f ɹ ɛ n t ʃ /. The phoneme describes the voiceness / unvoiceness as well as the position of articulators.

Phonemes are language-dependent, since the sounds produced in languages are not the same. We define a minimal pair as two words that differ by only one phoneme. For example, “kill” and “kiss”.

For the sake of completeness, here are the consonant and vowel phonemes in standard french:

There are several ways to see a word:

• as a sequence of phonemes
• as a sequence of graphemes (mostly a written symbol representing phonemes)
• as a sequence of morphemes (meaningful morphological unit of a language that cannot be further divided) (e.g “re” + “cogni” + “tion”)
• as a part-of-speech (POS) in morpho-syntax: grammatical class, e.g noun, verb, … and flexional information, e.g singular, plural, gender…
• as a syntax describing the function of the word (subject, object…)
• as a meaning

Therefore, labeling speech can be done at several levels:

And the labels may be time-algined if we know when they occur in speech.

The vocabulary is defined as the set of words in a specific task, a language or several languages based on the ASR system we want to build. If we have a large vocabulary, we talk about Large vocabulary continuous speech recognition (LVCSR) . If some words we encounter in production have never been seen in training, we talk about Out Of Vocabulary words (OOV).

We distinguish 2 types of speech recognition tasks:

• isolated word recognition
• continuous speech recognition, which we will focus on

Evaluation metrics

We usually evaluate the performance of an ASR system using Word Error Rate (WER). We take as a reference a manual transcript. We then compute the number of mistakes made by the ASR system. Mistakes might include:

• Substitutions, \(N_{SUB}\), a word gets replaced
• Insertions, \(N_{INS}\), a word which was not pronounced in added
• Deletions, \(N_{DEL}\), a word is omitted from the transcript

The WER is computed as:

The perfect WER should be as close to 0 as possible. The number of substitutions, insertions and deletions is computed using the Wagner-Fischer dynamic programming algorithm for word alignment.

Statistical historical approach to ASR

Let us denote the optimal word sequence \(W^{\star}\) from the vocabulary. Let the input sequence of acoustic features be \(X\). Stastically, our aim is to identify the optimal sequence such that:

This is known as the “Fundamental Equation of Statistical Speech Processing”. Using Bayes Rule, we can rewrite is as :

Finally, we suppose independence and remove the term \(P(X)\). Hence, we can re-formulate our problem as:

• \(argmax_W\) is the search space, a function of the vocabulary
• \(P(X \mid W)\) is called the acoustic model
• \(P(W)\) is called the language model

The steps are presented in the following diagram:

Feature extraction \(X\)

From the speech analysis, we should extract features \(X\) which are:

• robust across speakers
• robust against noise and channel effects
• low dimension, at equal accuracy
• non-redondant among features

Features we typically extract include:

• Mel-Frequency Cepstral Coefficients (MFCC), as desbribed here
• Perceptual Linear Prediction (PLP)

We should then normalize the features extracted to avoid mismatches across samples with mean and variance normalization.

Acoustic model

1. hmm-gmm acoustic model.

The acoustic model is a complex model, usually based on Hidden Markov Models and Artificial Neural Networks, modeling the relationship between the audio signal and the phonetic units in the language.

In isolated word/pattern recognition, the acoustic features (here \(Y\)) are used as an input to a classifier whose rose is to output the correct word. However, we take input sequence and should output sequences too when it comes to continuous speech recognition .

The acoustic model goes further than a simple classifier. It outputs a sequence of phonemes.

Hidden Markov Models are natural candidates for Acoustic Models since they are great at modeling sequences. If you want to read more on HMMs and HMM-GMM training, you can read this article . The HMM has underlying states \(s_i\), and at each state, observations \(o_i\) are generated.

In HMMs, 1 phoneme is typically represented by a 3 or 5 state linear HMM (generally the beginning, middle and end of the phoneme).

The topology of HMMs is flexible by nature, and we can choose to have each phoneme being represented by a single state, or 3 states for example:

The HMM supposes observation independence, in the sense that:

The HMM can also output context-dependent phonemes, called triphones. Triphones are simply a group of 3 phonemes, the left one being the left context, and the right one, the right context.

The HMM is trained using Baum-Welsch algorithm. The HMMs learns to give the probability of each end of phoneme at time t. We usually suppose the observations are generated by a mixture of Gaussians (Gaussian Mixture Models, GMMs) at each state, i.e:

The training of the HMM-GMM is solved by Expectation Maximization (EM). In the EM training, the outputs of the GMM \(P(X \mid W)\) are used as inputs for the GMM training iteratively, and the Viterbi or Baum Welsch algorithm trains the HMM (i.e. identifies the transition matrices) to produce the best state sequence.

The full pipeline is presented below:

2. HMM-DNN acoustic model

Latest models focus on hybrid HMM-DNN architectures and approach the acoustic model in another way. In such approach, we do not care about the acoustic model \(P(X \mid W)\), but we directly tackle \(P(W \mid X)\) as the probability of observing state sequences given \(X\).

Hence, back to the first acoustic modeling equation, we target:

The aim of the DNN is to model the posterior probabilities over HMM states.

Some considerations on the HMM-DNN framework:

• we usually take a large number of hidden layers
• the inputs features typically are extracted from large windows (up to 1-2 seconds) to have a large context
• early stopping can be used

You might have noticed that the training of the DNN produces posterior, whereas the Viterbi Backward-Forward algorithm requires \(P(X \mid W)\) to identify the optimal sequence when training the HMM. Therefore, we use Bayes Rule:

The probability of the acoustic feature \(P(X)\) is not known, but it just scales all the likelihoods by the same factor, and therefore does not modify the alignment.

The training of HMM-DNN architectures is based:

• E-step keeps DNN and HMM parameters constant and estimates the DNN outputs to produce scaled likelihoods
• M-step re-trains the DNN parameters on the new targets from E-step
• either using REMAP, with a similar architecture, except that the states priors are also given as inputs to the DNN

3. HMM-DNN vs. HMM-GMM

Here is a brief summary of the pros and cons of HMM/DNN and HMM/GMM:

4. End-to-end models

In End-to-end models, the steps of feature extraction and phoneme prediction are combined:

This concludes the part on acoustic modeling.

Pronunciation

In small vocabulary sizes, it is quite easy to collect a lot of utterances for each word, and the HMM-GMM or HMM-DNN training is efficient. However, “statistical modeling requires a sufficient number of examples to get a good estimate of the relationship between speech input and the parts of words”. In large-vocabulary tasks, we might collect 1 or even 0 training examples. t. Thus, it is not feasible to train a model for each word, and we need to share information across words, based on the pronunciation.

We consider words are being sequences of states \(Q\).

Where \(P(Q \mid W)\) is the pronunciation model .

The pronunciation dictionary is written by human experts, and defined in the IPA. The pronunciation of words is typically stored in a lexical tree, a data structure that allows us to share histories between words in the lexicon.

When decoding a sequence in prediction, we must identify the most likely path in the tree based on the HMM-DNN output.

In ASR, most recent approaches are:

• either end to end
• or at the character level

In both approaches, we do not care about the full pronunciation of the words. Grapheme-to-phoneme (G2P) models try to learn automatically the pronunciation of new words.

Language Modeling

Let’s get back to our ASR base equation:

The language model is defined as \(P(W)\). It assigns a probability estimate to word sequences, and defines:

• what the speaker may say
• the vocabulary
• the probability over possible sequences, by training on some texts

The contraint on \(P(W)\) is that \(\sum_W P(W) = 1\).

In statistical language modeling, we aim to disambiguate sequences such as:

“recognize speech”, “wreck a nice beach”

The maximum likelihood estimation of a sequence is given by:

Where \(C(w_1, ..., w_i)\) is the observed count in the training data. For example:

We call this ratio the relative frequency . The probability of a whole sequence is given by the chain rule of probabilities:

This approach seems logic, but the longer the sequence, the most likely it will be that we encounter 0’s, hence bringing the probability of the whole sequence at 0.

What solutions can we apply?

• smoothing: redistribute the probability mass from observed to unobserved events (e.g Laplace smoothing, Add-k smoothing)
• backoff: explained below

1. N-gram language model

But one of the most popular solution is the n-gram model . The idea behind the n-gram model is to truncate the word history to the last 2, 3, 4 or 5 words, and therefore approximate the history of the word:

We take \(n\) as being 1 (unigram), 2 (bigram), 3 (trigram)…

Let us now discuss some practical implementation tricks:

• we compute the log of the probabilities, rather than the probabilities themselves (to avoid floating point approximation to 0)
• for the first word of a sequence, we need to define pseudo-words as being the first 2 missing words for the trigram: \(P(I \mid <s><s>)\)

With N-grams, it is possible that we encounter unseen N-grams in prediction. There is a technique called backoff that states that if we miss the trigram evidence, we use the bigram instead, and if we miss the bigram evidence, we use the unigram instead…

Another approach is linear interpolate , where we combine different order n-grams by linearly interpolating all the models:

2. Language models evaluation metrics

There are 2 types of evaluation metrics for language models:

• extrinsic evaluation , for which we embed the language model in an application and see by which factor the performance is improved
• intrinsic evaluation that measures the quality of a model independent of any application

Extrinsic evaluations are often heavy to implement. Hence, when focusing on intrinsic evaluations, we:

• split the dataset/corpus into train and test (and development set if needed)
• learn transition probabilities from the trainig set
• use the perplexity metric to evaluate the language model on the test set

We could also use the raw probabilities to evaluate the language model, but the perpeplixity is defined as the inverse probability of the test set, normalized by the number of words. For example, for a bi-gram model, the perpeplexity (noted PP) is defined as:

The lower the perplexity, the better

3. Limits of language models

Language models are trained on a closed vocabulary. Hence, when a new unknown word is met, it is said to be Out of Vocabulary (OOV).

4. Deep learning language models

More recently in Natural Language Processing, neural network-based language models have become more and more popular. Word embeddings project words into a continuous space \(R^d\), and respect topological properties (semantics and morpho-syntaxic).

Recurrent neural networks and LSTMs are natural candidates when learning such language models.

The training is now done. The final step to cover is the decoding, i.e. the predictions to make when we collect audio features and want to produce transcript.

We need to find:

However, exploring the whole spact, especially since the Language Model \(P(W)\) has a really large scale factor, can be incredibly long.

One of the solutions is to explore the Beam Search . The Beam Search algorithm greatly reduces the scale factor within a language model (whether N-gram based or Neural-network-based). In Beam Search, we:

• identify the probability of each word in the vocabulary for the first position, and keep the top K ones (K is called the Beam width)
• for each of the K words, we compute the conditional probability of observing each of the second words of the vocabulary
• among all produced probabilities, we keep only the top K ones
• and we move on to the third word…

Let us illustrate this process the following way. We want to evaluate the sequence that is the most likely. We first compute the probability of the different words of the vocabulary to be the starting word of the sentence:

Here, we fix the beam width to 2, meaning that we only select the 2 most likely words to start with. Then, we move on to the next word, and compute the probability of observing it using conditional probability in the language model: \(P(w_2, w_1 \mid W) = P(w_1 \mid W) P(w_2 \mid w_1, W)\). We might see that a potential candidate, e.g. “The”, when selecting the top 2 candidates second words among all possible words, is not a possible path anymore. In that case, we narrow the search, since we know that the first must must be “a”.

And so on… Another approach to decoding is the Weighted Finite State Transducers (I’ll make an article on that).

Summary of the ASR pipeline

In their paper “Word Embeddings for Speech Recognition” , Samy Bengio and Georg Heigold present a good summary of a modern ASR architecture:

• Words are represented through lexicons as phonemes
• Typically, for context, we cluster triphones
• We then assume that these triphones states were in fact HMM states
• And the the observations each HMM state generates are produced by DNNs or GMMs

End-to-end approach

Alright, this article is already long, but we’re almost done. So far, we mostly covered historical statistical approaches. These approaches work very well. However, most recent papers and implementations focus on end-to-end approaches, where:

• we encode \(X\) as a sequence of contexts \(C\)
• we decode \(C\) into a sequence of words \(W\)

These approaches, also called encoder-decoder, are part of sequence-to-sequence models. Sequence to sequence models learn to map a sequence of inputs to a sequence of outputs, even though their length might differ. This is widely used in Machine Translation for example.

As illustrated below, the Encoder reduces the input sequence to a encoder vector through a stack of RNNs, and the decoder vector uses this vector as an input.

I will write more about End-to-end models in another article.

This is all for this quite long introduction to automatic speech recognition. After a brief introduction to speech production, we covered historical approaches to speech recognition with HMM-GMM and HMM-DNN approaches. We also mentioned the more recent end-to-end approaches. If you want to improve this article or have a question, feel free to leave a comment below :)

References:

• “Automatic speech recognition” by Gwénolé Lecorvé from the Research in Computer Science (SIF) master
• EPFL Statistical Sequence Processing course
• Stanford CS224S
• Rasmus Robert HMM-DNN
• A Tutorial on Pronunciation Modeling for Large Vocabulary Speech Recognition
• N-gram Language Models, Stanford
• Andrew Ng’s Beam Search explanation
• Encoder Decoder model
• Automatic Speech Recognition Introduction, University of Edimburgh

Speech Recognition: Everything You Need to Know in 2024

Speech recognition, also known as automatic speech recognition (ASR) , enables seamless communication between humans and machines. This technology empowers organizations to transform human speech into written text. Speech recognition technology can revolutionize many business applications , including customer service, healthcare, finance and sales.

In this comprehensive guide, we will explain speech recognition, exploring how it works, the algorithms involved, and the use cases of various industries.

If you require training data for your speech recognition system, here is a guide to finding the right speech data collection services.

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text.

Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents, dialects, and speech patterns.

What are the features of speech recognition systems?

Speech recognition systems have several components that work together to understand and process human speech. Key features of effective speech recognition are:

• Audio preprocessing: After you have obtained the raw audio signal from an input device, you need to preprocess it to improve the quality of the speech input The main goal of audio preprocessing is to capture relevant speech data by removing any unwanted artifacts and reducing noise.
• Feature extraction: This stage converts the preprocessed audio signal into a more informative representation. This makes raw audio data more manageable for machine learning models in speech recognition systems.
• Language model weighting: Language weighting gives more weight to certain words and phrases, such as product references, in audio and voice signals. This makes those keywords more likely to be recognized in a subsequent speech by speech recognition systems.
• Acoustic modeling : It enables speech recognizers to capture and distinguish phonetic units within a speech signal. Acoustic models are trained on large datasets containing speech samples from a diverse set of speakers with different accents, speaking styles, and backgrounds.
• Speaker labeling: It enables speech recognition applications to determine the identities of multiple speakers in an audio recording. It assigns unique labels to each speaker in an audio recording, allowing the identification of which speaker was speaking at any given time.
• Profanity filtering: The process of removing offensive, inappropriate, or explicit words or phrases from audio data.

What are the different speech recognition algorithms?

Speech recognition uses various algorithms and computation techniques to convert spoken language into written language. The following are some of the most commonly used speech recognition methods:

• Hidden Markov Models (HMMs): Hidden Markov model is a statistical Markov model commonly used in traditional speech recognition systems. HMMs capture the relationship between the acoustic features and model the temporal dynamics of speech signals.
• Estimate the probability of word sequences in the recognized text
• Convert colloquial expressions and abbreviations in a spoken language into a standard written form
• Map phonetic units obtained from acoustic models to their corresponding words in the target language.
• Speaker Diarization (SD): Speaker diarization, or speaker labeling, is the process of identifying and attributing speech segments to their respective speakers (Figure 1). It allows for speaker-specific voice recognition and the identification of individuals in a conversation.

Figure 1: A flowchart illustrating the speaker diarization process

• Dynamic Time Warping (DTW): Speech recognition algorithms use Dynamic Time Warping (DTW) algorithm to find an optimal alignment between two sequences (Figure 2).

Figure 2: A speech recognizer using dynamic time warping to determine the optimal distance between elements

5. Deep neural networks: Neural networks process and transform input data by simulating the non-linear frequency perception of the human auditory system.

6. Connectionist Temporal Classification (CTC): It is a training objective introduced by Alex Graves in 2006. CTC is especially useful for sequence labeling tasks and end-to-end speech recognition systems. It allows the neural network to discover the relationship between input frames and align input frames with output labels.

Speech recognition vs voice recognition

Speech recognition is commonly confused with voice recognition, yet, they refer to distinct concepts. Speech recognition converts  spoken words into written text, focusing on identifying the words and sentences spoken by a user, regardless of the speaker’s identity. 

On the other hand, voice recognition is concerned with recognizing or verifying a speaker’s voice, aiming to determine the identity of an unknown speaker rather than focusing on understanding the content of the speech.

What are the challenges of speech recognition with solutions?

While speech recognition technology offers many benefits, it still faces a number of challenges that need to be addressed. Some of the main limitations of speech recognition include:

Acoustic Challenges:

• Assume a speech recognition model has been primarily trained on American English accents. If a speaker with a strong Scottish accent uses the system, they may encounter difficulties due to pronunciation differences. For example, the word “water” is pronounced differently in both accents. If the system is not familiar with this pronunciation, it may struggle to recognize the word “water.”

Solution: Addressing these challenges is crucial to enhancing  speech recognition applications’ accuracy. To overcome pronunciation variations, it is essential to expand the training data to include samples from speakers with diverse accents. This approach helps the system recognize and understand a broader range of speech patterns.

• For instance, you can use data augmentation techniques to reduce the impact of noise on audio data. Data augmentation helps train speech recognition models with noisy data to improve model accuracy in real-world environments.

Figure 3: Examples of a target sentence (“The clown had a funny face”) in the background noise of babble, car and rain.

Linguistic Challenges:

• Out-of-vocabulary words: Since the speech recognizers model has not been trained on OOV words, they may incorrectly recognize them as different or fail to transcribe them when encountering them.

Figure 4: An example of detecting OOV word

Solution: Word Error Rate (WER) is a common metric that is used to measure the accuracy of a speech recognition or machine translation system. The word error rate can be computed as:

Figure 5: Demonstrating how to calculate word error rate (WER)

• Homophones: Homophones are words that are pronounced identically but have different meanings, such as “to,” “too,” and “two”. Solution: Semantic analysis allows speech recognition programs to select the appropriate homophone based on its intended meaning in a given context. Addressing homophones improves the ability of the speech recognition process to understand and transcribe spoken words accurately.

Technical/System Challenges:

• Data privacy and security: Speech recognition systems involve processing and storing sensitive and personal information, such as financial information. An unauthorized party could use the captured information, leading to privacy breaches.

Solution: You can encrypt sensitive and personal audio information transmitted between the user’s device and the speech recognition software. Another technique for addressing data privacy and security in speech recognition systems is data masking. Data masking algorithms mask and replace sensitive speech data with structurally identical but acoustically different data.

Figure 6: An example of how data masking works

• Limited training data: Limited training data directly impacts  the performance of speech recognition software. With insufficient training data, the speech recognition model may struggle to generalize different accents or recognize less common words.

Solution: To improve the quality and quantity of training data, you can expand the existing dataset using data augmentation and synthetic data generation technologies.

13 speech recognition use cases and applications

In this section, we will explain how speech recognition revolutionizes the communication landscape across industries and changes the way businesses interact with machines.

Customer Service and Support

• Interactive Voice Response (IVR) systems: Interactive voice response (IVR) is a technology that automates the process of routing callers to the appropriate department. It understands customer queries and routes calls to the relevant departments. This reduces the call volume for contact centers and minimizes wait times. IVR systems address simple customer questions without human intervention by employing pre-recorded messages or text-to-speech technology . Automatic Speech Recognition (ASR) allows IVR systems to comprehend and respond to customer inquiries and complaints in real time.
• Customer support automation and chatbots: According to a survey, 78% of consumers interacted with a chatbot in 2022, but 80% of respondents said using chatbots increased their frustration level.
• Sentiment analysis and call monitoring: Speech recognition technology converts spoken content from a call into text. After  speech-to-text processing, natural language processing (NLP) techniques analyze the text and assign a sentiment score to the conversation, such as positive, negative, or neutral. By integrating speech recognition with sentiment analysis, organizations can address issues early on and gain valuable insights into customer preferences.
• Multilingual support: Speech recognition software can be trained in various languages to recognize and transcribe the language spoken by a user accurately. By integrating speech recognition technology into chatbots and Interactive Voice Response (IVR) systems, organizations can overcome language barriers and reach a global audience (Figure 7). Multilingual chatbots and IVR automatically detect the language spoken by a user and switch to the appropriate language model.

Figure 7: Showing how a multilingual chatbot recognizes words in another language

• Customer authentication with voice biometrics: Voice biometrics use speech recognition technologies to analyze a speaker’s voice and extract features such as accent and speed to verify their identity.

Sales and Marketing:

• Virtual sales assistants: Virtual sales assistants are AI-powered chatbots that assist customers with purchasing and communicate with them through voice interactions. Speech recognition allows virtual sales assistants to understand the intent behind spoken language and tailor their responses based on customer preferences.
• Transcription services : Speech recognition software records audio from sales calls and meetings and then converts the spoken words into written text using speech-to-text algorithms.

Automotive:

• Voice-activated controls: Voice-activated controls allow users to interact with devices and applications using voice commands. Drivers can operate features like climate control, phone calls, or navigation systems.
• Voice-assisted navigation: Voice-assisted navigation provides real-time voice-guided directions by utilizing the driver’s voice input for the destination. Drivers can request real-time traffic updates or search for nearby points of interest using voice commands without physical controls.

Healthcare:

• Recording the physician’s dictation
• Transcribing the audio recording into written text using speech recognition technology
• Editing the transcribed text for better accuracy and correcting errors as needed
• Formatting the document in accordance with legal and medical requirements.
• Virtual medical assistants: Virtual medical assistants (VMAs) use speech recognition, natural language processing, and machine learning algorithms to communicate with patients through voice or text. Speech recognition software allows VMAs to respond to voice commands, retrieve information from electronic health records (EHRs) and automate the medical transcription process.
• Electronic Health Records (EHR) integration: Healthcare professionals can use voice commands to navigate the EHR system , access patient data, and enter data into specific fields.

Technology:

• Virtual agents: Virtual agents utilize natural language processing (NLP) and speech recognition technologies to understand spoken language and convert it into text. Speech recognition enables virtual agents to process spoken language in real-time and respond promptly and accurately to user voice commands.

Further reading

• Top 5 Speech Recognition Data Collection Methods in 2023
• Top 11 Speech Recognition Applications in 2023

External Links

• 1. Databricks
• 2. PubMed Central
• 3. Qin, L. (2013). Learning Out-of-vocabulary Words in Automatic Speech Recognition . Carnegie Mellon University.
• 4. Wikipedia

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10+ speech data collection services in 2024, top 5 speech recognition data collection methods in 2024, top 4 speech recognition challenges & solutions in 2024.

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Top 11 Voice Recognition Applications in 2024

Speech Recognition

8.3. speech recognition #, 8.3.1. introduction to asr #.

An ASR system produces the most likely word sequence given an incoming speech signal.  The statistical approach for speech recognition has dominated Automatic Speech Recognition (ASR) research over the last few decades leading to a number of successes. The problem of speech recognition is defined as the conversion of spoken utterances into textual sentences by a machine.  In the statistical framework, the Bayesian decision rule is employed to find the most probable word sequence, \( \hat H \) , given the observation sequence \( O = (o_1, . . . , o_T ) \) :

Following Bayes’ rule, the posterior probability in the above equation can be expressed as a conditional probability of the word sequence given the acoustic observations,  \( P(O|H) \) , multiplied by a prior probability of the word sequence,  \( P(H) \) , and normalized by the marginal likelihood of observation sequences, \( P(O) \) :

The marginal probability, \( P(O) \) , is discarded in the second equation since it is constant with respect to the ranking of hypotheses, and hence does not alter the search for the best hypothesis.  \( P(O|H) \) is calculated by the acoustic model and  \( P(H) \) is modeled by the language model.

8.3.2. Component of ASR #

Feature Extraction: It converts the speech signal into a sequence of acoustic feature vectors. These observations should be compact and carry sufficient information for recognition in the later stage.

Acoustic Model: It Contains a statistical representation of the distinct sounds that make up each word in the Language Model or Grammar.  Each distinct sound corresponds to a phoneme.

Language Model: It contain a very large list of words and their probability of occurrence in a given sequence.

Decoder: It is a software program that takes the sounds spoken by a user and searches the acoustic Model for the equivalent sounds.  When a match is made, the decoder determines the phoneme corresponding to the sound.  It keeps track of the matching phonemes until it reaches a pause in the users speech.  It then searches the language model  for the equivalent series of phonemes.  If a match is made, it returns the text of the corresponding word or phrase to the calling program.

8.3.3. Types of ASR #

Speech recognition systems can be classified on the basis of the constraints under which they are developed and which they consequently impose on their users. These constraints include: speaker dependence, type of utterance, size of the vocabulary, linguistic constraints, type of speech and environment of use. We will describe each constraint as follows:

Speaker Dependence : Speaker dependent speech recognition system requires the user to be involved in its development whereas speaker independent systems do not. Speaker independent systems can be used by anybody. Speaker dependent systems usually perform much better than speaker independent systems. This is due to the fact that the acoustic variations among different speakers are very difficult to describe and model. There are approaches to make a system speaker independent. The first one is the use of multiple representations for each reference to capture the speaker variation and the second one is the speaker adaptation approach.

Type of Utterance : A speech recognizer may recognize every word independently. It may require its user to speak each word in a sentence separating them by artificial pause or it may allow the user to speak in a natural way. The first type of system is categorized as an isolated word recognition system. It is the simplest form of a recognition strategy. It can be developed using word-based acoustic models without any language model. If, however, the vocabulary increases sentences composed of isolated words to be recognized, the use of sub-word acoustic models and language models become important. The second one is the continuous speech recognition systems. It allows the users to utter the message in a relatively or completely unconstrained manner. Such recognizers must be capable of performing well in the presence of all the co-articulatory effects. Developing continuous speech recognition systems is, therefore, the most difficult task. This is due to the following properties of continuous speech:  word boundaries are unclear in continuous speech; and co-articulatory effects are much stronger in continuous speech

Vocabulary Size : The number of words in the vocabulary is a constraint that makes a speech recognition system small, medium or large. As a rule of thumb, small vocabulary systems are those which have a vocabulary size in the range of 1-99 words; medium, 100-999 words; and large, 1000 words or more. Large vocabulary speech recognition systems perform much worse compared to small vocabulary systems due to different factors such as word confusion that increases with the number of words in the vocabulary. For small vocabulary recognizer, each word can be modeled. However, it is not possible to train acoustic models for thousands of words separately because we cannot have enough training speech and storage for parameters of the speech that is needed. The development of large vocabulary recognizer, therefore, requires the use of sub-word units. On the other hand, the use of sub-word units results in performance degradation since they cannot capture co-articulatory effects as words do. The search process in large vocabulary recognizer also uses pruning instead of performing a complete search.

Type of Speech: A speech recognizer can be developed to recognize only read speech or to allow the user speak spontaneously. The latter is more difficult to build than the former due to the fact that spontaneous speech is characterized by false starts, incomplete sentences, unlimited vocabulary and reduced pronunciation quality. The primary difference in recognition error rates between read and spontaneous speech are due to disfluencies in spontaneous speech. Disfluencies in spontaneous speech can be characterized by long pauses and mispronunciations. Spontaneous is, therefore, both acoustically and grammatically difficult to recognize.

Environment : Speech recognizer may require the speech to be clean from environmental noises, acoustic distortions, microphones and transmission channel distortions or they may ideally handle any of these problems. While current speech recognizer give acceptable performance in carefully controlled environments, their performance degrades rapidly when they are applied in noisy environments. This noise can take the form of speech from other speakers; equipment sounds, air conditioners or others. The noise might also be created by the speaker himself in a form of lip smacks, coughs or sneezes.

8.3.4. Models for Large Vocabulary Speech Recognition (LVCSR) #

LVCSR can be divided into two categories: HMM-based model and the end-to-end model.

8.3.4.1. HMM-Based Model #

The HMM-based model has been the main LVCSR model for many years with the best recognition accuracy. An HMM-based model is divided into three parts:acoustic, pronunciation and language model. In HMM based model, each model is independent of each other and plays a different role. While the acoustic model models the mapping between speech input and feature sequence, the pronunciation model maps between phonemes (or sub-phonemes) to graphemes, and the language model maps the character sequence to fluent final transcription.

Acoustic Model:   In the acoustic model, the observation probability  is generally represented by GMM. The posterior probability distribution of hidden state can be calculated by DNN method. These two different calculations result into two different models, namely HMM-GMM and HMM-DNN. HMM-GMM model was a general structure for many speech recognition systems. However, with the development of deep learning technology, DNN is introduced into speech recognition for acoustic modeling. DNN has been used to calculate the posterior probability of the HMM state replacing the conventional GMM observation probability. Thus, HMM-GMM model is replaced by HMM-DNN since HMM-GMM provides better results compared to HMM-GMM and becomes state-of-the-art ASR model. In the HMM-based model, different modules use different technologies and have different roles. While the HMM is mainly used to do dynamic time warping at the frame level, GMM and DNN are used to calculate emission probability of HMM hidden states.

Pronunciation Model:  Its main objective is achieve the connection between acoustic sequence and language sequence. The dictionary includes various levels of mapping, such as pronunciation to phone, phone to trip-hone. The dictionary is used to achieve structural mapping and map the probability calculation relationship.

Language Model: It contains rudimentary syntactic information. Its aim is to predict the likelihood of specific words occurring one after another in a given language. Typical recognizers use n-gram language models. An n-gram contains the prior probability of the occurrence of a word (unigram), or of a sequence of words (bigram, trigram etc.):

unigram probability \( P(w_i) \)

bigram probability \( P(w_i|w_{i−1}) \)

ngram probability \( P(w_n|w_{n−1},w_{n−2}, …,w_1) \)

Limitations of HMM-models

The training process is complex and difficult to be globally optimized. HMM-based model often uses different training methods and data sets to train different modules. Each module is independently optimized with their own optimization objective functions which are generally different from the true LVCSR performance evaluation criteria. So the optimality of each module does not necessarily bring global optimality.

Conditional independence assumptions. To simplify the model’s construction and training, the HMM-based model uses conditional independence assumptions within HMM and between different modules. This does not match the actual situation of LVCSR.

8.3.4.2. End-to-End Model #

Because of the  above-mentioned shortcomings of the HMM-based model and coupled with the promotion of deep learning technology, more and more works began to study end-to-end LVCSR. The end-to-end model is a system that directly maps input audio sequence to sequence of words or other graphemes.

Most end-to-end speech recognition models include the following parts: the encoder maps speech input sequence to feature sequence; the aligner realizes the alignment between feature sequence and language; the decoder decodes the final identification result. Note that this division does not always exist since end-to-end itself is a complete structure. Contrary to the HMM-based model that  consists of multiple modules, the end-to-end model replaces multiple modules with a deep network, realizing the direct mapping of acoustic signals into label sequences without carefully-designed intermediate states. In addition to this, there is no need to perform posterior processing on the output.

Compared to HMM-based model, the main characteristics of end-to-end LVCSR are:

Multiple modules are merged into one network for joint training. The benefit of merging multiple modules is there is no need to design many modules to realize the mapping between various intermediate states. Joint training enables the end-to-end model to use a function that is highly relevant to the final evaluation criteria as a global optimization goal, thereby seeking globally optimal results.

It directly maps input acoustic signature sequence to the text result sequence, and does not require further processing to achieve the true transcription or to improve recognition performance . But, in the HMM-based models, there is usually an internal representation for pronunciation of a character chain.

These features of of end-to-end LVCSR model enables to greatly simplify the construction and training of speech recognition models.

The end-to-end model are mainly divided into three different categories depending on their implementations of soft alignment:

CTC-based: It first enumerates all possible hard alignments. Then, it achieves soft alignment by aggregating these hard alignments. CTC assumes that output labels are independent of each other when enumerating hard alignments.

RNN-transducer: It also enumerates all possible hard alignments and then aggregates them for soft alignment. But unlike CTC, RNN-transducer does not make independent assumptions about labels when enumerating hard alignments. Thus, it is different from CTC in terms of path definition and probability calculation.

Attention-based: This method no longer enumerates all possible hard alignments, but uses attention mechanism to directly calculate the soft alignment information between input data and output label.

CTC-Based End-to-End Model

Although HMM-DNN provides still state-of-the-art results, the role played by DNN is limited. It is mainly used to model the posterior state probability of HMM’s hidden state. The time-domain feature is still modeled by HMM. When attempting to model time-domain features using RNN or CNN instead of HMM, it faces a data alignment problem: both RNN and CNN’s loss functions are defined at each point in the sequence, so in order to be able to perform training, it is necessary to know the alignment relation between RNN output sequence and target sequence.

CTC makes it possible to make fuller use of DNN in speech recognition and build end-to-end models, which is a breakthrough in the development of end-to-end method. Essentially, CTC is a loss function, but it solves hard alignment problem while calculating the loss. CTC mainly overcomes the following two difficulties for end-to-end LVCSR models:

Data alignment problem. CTC no longer needs to segment and align training data. This solves the alignment problem so that DNN can be used to model time-domain features, which greatly enhances DNN’s role in LVCSR tasks.

Directly output the target transcriptions. Traditional models often output phonemes or other small units, and further processing is required to obtain the final transcriptions. CTC eliminates the need for small units and direct output in final target form, greatly simplifying the construction and training of end-to-end model.

RNN-Transducer End-to-End Model

CTC has two main deficiencies in CTC which hinder its effectiveness:

CTC cannot model interdependencies within the output sequence because it assumes that output elements are independent of each other. Therefore, CTC cannot learn the language model. The speech recognition network trained by CTC should be treated as only an acoustic model.

CTC can only map input sequences to output sequences that are shorter than it. Thus, it is powerless for scenarios where output sequence is longer.

For speech recognition, the first point has huge impact. RNN-transducer was proposed to solve the above-mentioned shortcomings of CTC. Theoretically, it can map an input to any finite, discrete output sequence. Interdependencies between input and output and within output elements are also jointly modeled.

The RNN-transducer has many similarities with CT: their main goals is to solve the forced segmentation alignment problem in speech recognition; they both introduce a “blank” label; they both calculate the probability of all possible paths and aggregate them to get the label sequence. However, their path generation processes and the path probability calculation methods are completely different. This gives rise to the advantages of RNN-transducer over CTC.

8.3.5. Types of errors made by speech recognizers #

Though ASR research has come a long way, today’s systems are far from being perfect. Speech recognizer are brittle and make errors due to various causes. Most errors made by ASR systems fall into one of the following categories:

Out-of-vocabulary (OOV) errors : Current state of the art speech recognizers have closed vocabularies. This means that they are incapable of recognizing words outside their training vocabulary. Besides misrecognition, the presence of an out-of-vocabulary word in input utterance causes the system to err to a similar word in its vocabulary. Special techniques for handling OOV words have been developed for HMM-GMM and neural ASR systems (see, e.g., [ Zhang, 2019 ] ).

Homophone substitution : These errors can occur if more than one lexical entry has the same pronunciation (phone sequence), i.e., they are homophones. While decoding, homophones may be confused with one another causing errors. In general, a well-functioning language model should disambiguate homophones based on the context.

Language model bias : Because of an undue bias  towards the language model (effected by a high relative weight on the language model), the decoder may be forced to reject the true hypothesis in favor of a spurious candidate with high language model probability. These errors may occur along with analogous acoustic model bias.

Multiple acoustic problems : This is a broad category of errors comprising those due to bad pronunciation entries; disfluency, mispronunciation by the speaker himself/herself, or errors made by acoustic models (possibly due to acoustic noise, data mismatch between training and usage etc.).

8.3.6. Challenges of ASR #

Recent advances in ASR has brought automatic speech recognition accuracy close to human performance in many practical tasks. However, there are still challenges:

Out-of-vocabulary words are difficult to recognize correctly

Varying environmental noises impair recognition accuracy.

Overlapping speech is problematic for ASR system.

Recognizing children’s speech and the speech of people with speech production disabilities is suboptimal with regular training data.

DNN-based models usually require a lot of data for training, in the order of thousands of hours. End-to-end models may need up to 100,000h of speech to reach high performance.

Uncertainty self-awareness is limited: typical ASR systems always output the most likely word sequence instead of reporting if some part of the input was incomprehensible or highly uncertain.

8.3.7. Evaluation #

The performance of an ASR system is measured by comparing the hypothesized transcriptions and reference transcriptions. Word error rate (WER) is the most widely used metric. The two word sequences are first aligned using a dynamic programming-based string alignment algorithm. After the alignment, the number of deletions (D), substitutions (S), and insertions (I) are determined. The deletions, substitutions and insertions are all considered as errors, and the WER is calculated by the rate of the number of errors to the number of words (N) in the reference.

Sentence Error Rate (SER) is also sometime used to evaluate the performance of ASR systems. SER computes the percentage of sentences with at least one error.

8.3.7.1. References #

Xiaohui Zhang. Strategies for Handling Out-of-Vocabulary Words in Automatic Speech Recognition . PhD thesis, Johns Hopkins University, 2019. URL: http://jhir.library.jhu.edu/handle/1774.2/62275 .

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An introduction to speech recognition

Speech recognition

A review of the development of speech recognition from its early inception, the increasing role of artificial intelligence and how it is impacting upon the day-to-day operations of today’s businesses.

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability which enables a program to identify human speech and convert it into readable text.

Whilst the more basic speech recognition software has a limited vocabulary, we are now seeing the emergence of more sophisticated software can handle natural speech, different accents and various languages, whilst also achieving much higher accuracy rates. We are also using speech recognition technology much more in our everyday lives, with an increasing number of people taking advantage of digital assistants like Google Home, Siri, and Amazon Alexa.

So, how has the technology evolved, how does it work and what are the opportunities for businesses and professionals across numerous industries and sectors to exploit speech recognition in the everyday work?

Here’s a quick overview of how speech recognition has developed from the early prototypes:

• 1952 - The first-ever speech recognition system, known as “Audrey” was built by Bell Laboratories. It was capable of recognising the sound of a spoken digit – zero to nine – with more than 90% accuracy when uttered by a single voice (its developer HK David).
• 1962 – IBM created the “Shoebox, a device that could recognise and differentiate between 16 spoken English words.
• 1970s - As part of a US Department of Defence-funded program, Carnegie Mellon University developed the “Harpy” system that could recognise entire sentences and had a vocabulary of 1,011 words.
• 1980s – IBM developed a voice-activated typewriter called Tangora which used a statistical prediction model for word identification with a vocabulary of 20,000 words.
• 1996 – IBM were involved again, this time with VoiceType Simply Speaking, a speech recognition application that had a 42,000 word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.
• 2000s – With speech recognition now achieving close to an 80% accuracy rate, voice assistants (also commonly referred to as digital assistants) came to the fore, firstly Google Voice to be followed a few years after by Apple’s launch of Siri and Amazon coming out with Alexa.

How it works

A wide range of speech recognition applications and devices are available, with the more advanced solutions now use Artificial Intelligence (AI) and machine learning. They are typically based on the following models:

• Acoustic models – making it possible to distinguish between the voice signal and the phonemes (the units of sound).
• Pronunciation models – defining how the phonemes can be combined to make words.
• Language models – matching sounds with word sequences in order to distinguish between words that sound the same.

Initially, the Hidden Markov Model (HMM) was widely adopted as an acoustic modelling approach. However, it has largely been replaced by deep neural networks. The use of deep learning in speech recognition has had the effect of significantly lowering the word error rate.

Word error rate

A key factor in speech recognition technology is its accuracy rate, commonly referred to as the word error rate (WER). A number of factors can impact upon the WER, for example different speech patterns, speaking styles, languages, dialects, accents and phrasings. The challenge for the software algorithms that process and organise audio into text are to address these effectively, whilst also being able to separate the spoken audio from background noise that often accompanies the signal.

The application of speech recognition

Thanks to laptops, tablets and smartphones, together with the rapid development of AI, speech recognition software has entered all aspects of our everyday life. Examples include:

Virtual assistants

These integrate with a range of different platforms and enable us to command our devices just by talking. At the personal level examples include Siri, Alexa and Google Assistant. In the office they can be used to complement the work of human employees by taking responsibility for repetitive, time-consuming tasks and allowing employees to focus their energy on more high-priority activities.

Voice search

Speech recognition technology is not only impacting the way businesses perform daily tasks but also how their customers are able to reach them. Voice search is typically used on devices such as smartphones, laptops and tablets, allowing users to input a voice-based search query instead of typing their query into a search engine. The differences between spoken and typed queries can cause different SERP (search engine results page) results since the way we speak creates new voice search keywords that are more conversational than typed keywords.

Speech to text solutions

And finally, the most significant area as far as business users are concerned is speech to text software. This area is growing rapidly, due in no small part to the availability of cloud-based solutions that are enabling users to access fully featured versions of speech to text apps from the smartphones or tablets irrespective of their locations. Furthermore, speech recognition technology can reduce repetitive tasks and free up professionals to use their time more productively, whilst also allowing businesses to save money by automating processes and doing administrative tasks more quickly.

Learn more about our speech to text service

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Introduction to Speech Recognition Algorithms: Learn How It Has Evolved

Rev › Blog › Speech to Text Technology › Introduction to Speech Recognition Algorithms: Learn How It Has Evolved

Consider all the ways to cook an egg. Using mostly the same ingredients, we can prepare them sunny side up or over easy. We can make a diner-style omelet, a fancy French omelet , or even a Japanese rolled omelet . Maybe they differ slightly in seasoning or the type of fat we use to coat the pan, but the real distinction between these preparations is tech nique.

We see this same theme play out in computer science with the development of new and improved speech recognition algorithms. While many technologists rightly attribute the recent “ AI explosion ” to the rise of big data alongside advances in computing power , especially graphics processing units (GPUs) for machine learning (ML), we can’t ignore the profound effects of hard work by data scientists, researchers, and academics around the world. Yes, using a stove instead of a campfire helps us to cook an egg, but that doesn’t tell the whole story when it comes to differentiating a soft-boiled egg from a souffle.

That’s why we’re going to give you a quick rundown of speech recognition algorithms, both past and present. We’ve done away with the dense jargon and indecipherable math that fills so many similar articles on the web. This introduction is written for you, the curious reader who doesn’t have a PhD in computer science.  

Before we get started, let’s return for a moment to our cooking analogy to simplify our central concept: the algorithm. Just like a recipe, an algorithm is nothing more than a set of ordered steps. First do this, then do that. Computer algorithms usually rely on a complex series of conditionals—if the pancake batter is too thick, add more milk; else add more flour. You get the point.

The Old Way

Automatic speech recognition (ASR) is one of the oldest applications of artificial intelligence because it’s so clearly useful. Being able to use voice to give a computer input is much easier and more intuitive than using a mouse, keyboard, or touchscreen. While we’re not going to be able to account for every method we’ve tried to get computers to become better listeners, we are going to give you an overview of the two major algorithms that dominated ASR until very recently.

Keep in mind that these speech recognition engines were Frankenstein creations that required multiple models to turn speech into text. An acoustic model digested soundwaves and translated them into phonemes , the basic building blocks of language, while a language model pieced them together to form words and a pronunciation model glued them together by attempting to account for the vast variations in speech that result from everything from geography to age. The result of this multi-pronged approach was a system that was as fragile as it was finicky. We’re sure you’ve dealt with this mess on a customer service hotline.

These systems primarily relied on two types of algorithms. First, n-gram models use the previous n words as context to try to figure out a given word. So, for instance, if it looks at the previous two words, we call it a bi-gram system and n=2. While higher values for n lead to greater accuracy since the computer has more context to look at, it simply isn’t practical to use a large number for n because the computational overhead is too much. Either we need such a powerful computer that the costs aren’t worth it, or the system becomes sluggish to the point of unusability.

The other algorithm is the Hidden Markov Model (HMM), which basically just goes in the opposite direction. Instead of looking backwards, HMMs look forwards. Without including any knowledge of the previous state—in our case, the words that came before the word in question—a HMM algorithm uses probabilities and statistics to guess what comes next. The “hidden” part means that we can include information about the target word that’s not obviously apparent, such as a part-of-speech tag (verb, noun, etc.). If you’ve ever used an auto-complete feature, then you’ve seen an HMM in action.

The New Way

Today’s state-of-the-art speech recognition algorithms leverage deep learning to create a single, end-to-end model that’s more accurate , faster, and easier to deploy on smaller machines like smart phones and internet of things (IoT) devices such as smart speakers. The main algorithm that we use is the artificial neural network, a many-layered (hence deep) architecture that’s loosely modeled on the workings of our brains.

Larry Hardesty at MIT gives us a good overview of how the magic happens: “To each of its incoming connections, a node will assign a number known as a ‘weight.’ When the network is active, the node receives a different data item—a different number—over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node ‘fires,’ which in today’s neural nets generally means sending the number—the sum of the weighted inputs—along all its outgoing connections.”

While most neural networks “feed-forward,” meaning that nodes only send their output to nodes that are further down in the chain, the specific algorithms that we use for speech processing work a little differently. Dubbed the Recurrent Neural Network (RNN), these algorithms are ideal for sequential data like speech because they’re able to “remember” what came before and use their previous output as input for their next move. Since words generally appear in the context of a sentence, knowing what came before and recycling that information into the next prediction goes a long way towards accurate speech recognition.

Now, there’s one last algorithm that we need to mention to give you a full overview of speech recognition algorithms. This one solves a very specific problem with training speech recognition models. Remember that ML models learn from data; for instance, an image classifier can tell the difference between cats and dogs after we feed it pictures that we label as either “cat” or “dog.” For speech recognition, this amounts to feeding it hours upon hours of audio and the corresponding ground-truth transcripts that were written by a human transcriptionist.

But how does the machine know which words in the transcript correspond to which sounds in the audio? This problem is even further compounded by the fact that our rate of speaking is anything but constant. Maybe we slow down for effect or speed up when we realize that our allotted presentation time is almost through. Either way, the rate at which we say the same word can vary dramatically. In technical terms, we call this a problem of alignment.

To solve this conundrum, we employ Connectionist Temporal Classification (CTC). This algorithm uses a probabilistic approach to align the labels (transcripts) with the training data (audio). How exactly this works is beyond the scope of this article, but suffice to say that this is a key ingredient for training a neural network to perform speech recognition tasks.

When we add it all up, recurrent neural networks alongside CTC have enabled huge breakthroughs in speech recognition technology. Our systems are able to handle large vocabularies, incredible variations in speaker dialect and pronunciation, and even operate in real-time thanks to these algorithms. 

In truth, there’s really no single factor that’s responsible for these advances. Yes, the software that we’ve described plays a huge role, but the hardware it runs on and the data it learns from are all equal parts of the equation. These factors have a symbiotic relationship; they grow and improve together. It’s a virtuous feedback loop.

However, that also means getting started can feel harder than ever. Between the vast quantities of data, the complex algorithms, and access to supercomputers in the cloud, established players in the industry have a huge head-start on anyone who is trying to catch up.

And that’s exactly why we’ve decided to offer our text-to-speech API , Rev.ai, to the developer community. You don’t have to build a full ASR engine to build a custom application that includes state-of-the-art voice integration. Get started today.

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Topic and Readings

Overview of Course, Intro to Probability Theory, and ASR Background: N-gram Language Modeling

TTS: Background (part of speech tagging, machine learning, classification, NLP) and Text Normalization

• Read this only if you haven't had phonetics: J+M New Chapter 7: Phonetics
• Note that there is a lot of reading for today.
• J+M New Chapter 8 Speech Synthesis, pages 1-10
• J+M New Chapter 5 Word Classes and Part-of-Speech Tagging, pages 1-36, but skip section 5.6
• These notes on learning decision trees, up to the section called "Assessing the Performance"
• Optional Advanced Reading: Chapter 4 "Text Segmentation and Organisation" from from Taylor, Paul. 2007. Text-to-Speech Synthesis
• Optional Advanced Reading: Chapter 5 "Text Decoding" from Taylor, Paul. 2007. Text-to-Speech Synthesis

TTS: Grapheme-to-phoneme, Prosody (Intonation, Boundaries, and Duration) and the Festival software

• Chapter 8 "Pronunciation" from Taylor, Paul. 2007. Text-to-Speech Synthesis -->
• Read sections 1, 2, 3, 4, 5, and 6.1 and 6.1.1 from Alan Black's lecture notes on TTS and Festival.
• You should be looking at the Festival manual . You don't have to read the whole thing through, but you should skim it so you know where things are in the manual.
• For those who don't know Scheme, (used as Festival's scripting language): Introduction to Scheme for C Programmers, from Cal Tech .

TTS: Waveform Synthesis (Diphone and Unit Selection Synthesis)

• J+M New Chapter 8, pages 25-end
• Optional Advanced Reading: Chapter 16 "Unit Selection Synthesis" from Taylor, Paul. 2007. Text-to-Speech Synthesis
• Optional Advanced Reading: Optional: Section 7 from Alan Black's lecture notes on TTS and Festival.
• Optional Advanced Reading: The rest of Section 6 of Alan Black's lecture notes

ASR: Noisy Channel Model, Bayes, HMMs, Forward, Viterbi

• J+M New Chapter 6: Hidden Markov Models, pages 1-20
• J+M New Chapter 9: Automatic Speech Recognition, pages 1-12

ASR: Feature Extraction and Acoustic Modeling, Evaluation

• J+M New Chapter 10: Speech Recognition: Advanced Topics pages 11-16

ASR: Learning (Baum-Welch) and Disfluencies

• J+M New Chapter 10: Speech Recognition: Advanced Topics pages 1-11

An introduction to speech and speaker recognition

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