Speaker Recognition

What's inside

Learning objectives

Basic concepts and core algorithms in speaker recognition
Audio processing and acoustics
Machine learning and deep learning basics

Coding practice and toolkits for audio and speech
Python and pytorch for machine learning
Building a speaker recognition system from scratch

Basic concepts and core algorithms in speaker recognition
Audio processing and acoustics
Machine learning and deep learning basics
Coding practice and toolkits for audio and speech
Python and pytorch for machine learning
Building a speaker recognition system from scratch

Syllabus

Introduction to this course

Hello fellow scholars, my name is Quan Wang. I'm currently a Staff Software Engineer at Google, leading the "Speaker, Voice and Language" team. I was also a former machine learning scientist of the Amazon Alexa team.

I will be your instructor of this course, to share my knowledge and experience about speaker recognition techniques with you, and help you get prepared for your academic and career goals.

I have been working for more than 8 years in the voice identity industry.

At Google, my team and I had been developing lots of successful products. We filed lots of valuable patents, and published many impactful papers at top conferences. We frequently hit the headlines in tech news. I published a textbook about voice identity techniques, which became one of the bestselling books about AI in China. This book also won me the Distinguished Author of Year 2020 Award. So, many people consider me as kind of a "successful" scientist.

However, when I look back how I started this journey, it wasn't quite so pleasant at the very beginning.

Most of my undergraduate and Ph.D. research work had been focusing on computer vision and image processing. When I first started working on speech and speaker recognition at Amazon, I was under huge pressure. This pressure was not from my manager or anyone else. It was from myself, by realizing my knowledge and expertise really does not match what is required in my projects.

Every time I have meetings with different teams, or review people's code, documents, I don't know what people are talking about. I've even never heard of the terminologies and the acronyms, while people naturally assume you understand them. I was just feeling that I was the dullest person in the company. That experience was really terrible.

And that was the time when I really wished someone could just teach me the basic concepts in audio processing, speech, and speaker recognition. I searched the internet, but unfortunately, there were no such online courses.

I bought lots of books, and read lots of papers, online articles and tech blogs. However, there was really nothing that systematically introduces speaker recognition. Everything I could find was just fragmented information. Besides, most of the papers were very obscure, too difficult to follow for someone new in the field. Many online articles or blogs were unprofessional, even with obvious mistakes. And most technical books were already outdated when they were published.

And that is the reason why I decided to spend several years developing this course, to help anyone interested in speaker recognition techniques, to easily start working in this domain in the most frictionless way, and avoid all the frustrations that I experienced myself. Don't waste your time on fragmented, unprofessional, or outdated information. In this course, I will systematically walk you through the basic concepts from acoustics, audio processing, deep learning, to speaker recognition, and its various applications.

To summarize, what I'm going to teach in this course, is what I wish someone could have taught me many years ago - the core algorithms and engineering practice of speaker recognition.

What is the expected outcome from this course?

Well, that really depends on who you are. This course mainly targets 3 different groups of audience. Group 1, students and researchers; group 2, industry audiences; and group 3, general audiences.

Group 1 audience should include senior college students, graduate students, as well as postdocs and technical staff members working at research institutes. For these audiences, even if you know nothing about any speech technology right now, at the end of this course, you should be able to very confidently talk about topics like audio processing, speaker recognition, deep learning, even the very latest work.

If you haven't done any research before, at the end of this course, you should be comfortable to make a decision whether you want to do your thesis in speaker recognition. If you go to a top conference like ICASSP or Interspeech, you should be comfortable to chat with people and ask people questions without fear.

In group 2, the industry audiences typically include software engineers, system architects, product managers who work on products and services that are related to voice identity techniques. For these audiences, taking this course will help to complement your current knowledge system in this domain, and help you follow the latest trends in academia. This will make you more competitive in your current position, and take your career to the next level.

And group 3, the general audiences. For this group of audience, the purpose of taking this course might be different from the other groups. Many of the lectures in this course could be too technical for the general audience who may not have the corresponding background in mathematics or computer science. For these audiences, it is OK to skip some lectures and the exercises, and only watch those lectures talking about history, applications, and high level concepts. This will help you get a clear big picture of the speaker recognition industry, expand your general knowledge, and maybe make better investment decisions. You will sound like a pro when you chat with your family and friends.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Includes hands-on practices and coding examples, which allows learners to master the topics and build their own speaker recognition system from scratch

Covers basics of acoustics, perception, audio processing, signal processing, and feature extraction, which means learners do not need a prior background in these domains

Taught by an instructor with 8 years of experience in the voice identity industry at Google and Amazon, which suggests real-world insights and practical knowledge

Explores modern speaker recognition with deep learning, including inference logic, loss functions, and neural network topologies, which are essential for current applications

Requires familiarity with Python and PyTorch for machine learning, which may require learners to acquire these skills before or during the course

Focuses on building speaker recognition systems based on acoustic features and machine learning models, which are highly relevant to industry applications

Reviews summary

Speaker recognition: from basics to deep learning

According to learners, this course offers a solid introduction to the field of speaker recognition, taught by an experienced industry expert. Students praise the instructor's ability to explain complex topics clearly, bridging the gap between theory and practical application. The coverage includes foundational concepts in audio processing and earlier techniques like GMMs, progressing to modern methods utilizing deep learning. Many find the included hands-on coding examples and labs particularly helpful for building practical skills. However, some learners note that despite the course covering basics, a strong background in mathematics or signal processing can be beneficial, as the pace can feel fast at times. The course is considered highly relevant for those looking to work in voice identity or related AI fields.

Explains basics from audio to early models.

"The initial modules on acoustics and signal processing were a great refresher or introduction."

"Understanding the history and early methods like GMM-UBM was very valuable context."

"Provides a good grounding in the fundamental audio processing techniques needed."

Good overview of the speaker recognition field.

"This course gives a very comprehensive overview of speaker recognition techniques."

"Excellent starting point for anyone wanting to enter the voice identity domain."

"It covers the pipeline from features to evaluation well."

Strong focus on deep learning approaches.

"The sections on deep learning for speaker recognition are up-to-date and cover relevant modern techniques."

"Appreciate the deep dive into modern neural network architectures and loss functions used today."

"Finally, a course that explains the latest deep learning methods in speaker recognition clearly."

Coding labs and examples are practical and useful.

"The hands-on exercises and coding examples were incredibly helpful for reinforcing the concepts."

"Building systems from scratch in the labs solidified my understanding more than just lectures."

"Really liked the practical aspect of the course, especially the PyTorch examples."

Instructor's industry experience is a key strength.

"The instructor is very experienced in this field and explains concepts clearly based on his real-world work."

"Quan Wang shares valuable insights from his Google and Amazon Alexa background, making the content highly relevant."

"I found the lectures delivered by the award-winning author to be very insightful and well-structured."

"Learned a lot from the instructor's practical perspective gained from years in the industry."

Pace can be challenging without a strong background.

"Although basics are covered, a solid foundation in math and signal processing is highly recommended to keep up."

"The course moves quite fast, especially in the later sections. Be prepared to pause and rewatch lectures."

"Found it challenging without prior signal processing knowledge, contrary to the description."

"Requires some existing familiarity with machine learning concepts to fully grasp."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Speaker Recognition | By Award Winning Textbook Author with these activities:

Review Audio Signal Processing Fundamentals

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Reinforce your understanding of core signal processing concepts, which are essential for grasping feature extraction techniques used in speaker recognition.

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Review key concepts like Fourier transforms, sampling rate, and windowing.
Work through practice problems involving signal manipulation and analysis.
Consult online resources or textbooks for clarification on challenging topics.

Review Audio Signal Processing Fundamentals

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Reinforce your understanding of core signal processing concepts, which are essential for grasping feature extraction techniques used in speaker recognition.

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Review key concepts like Fourier transforms, sampling rate, and windowing.
Work through practice problems involving signal manipulation and analysis.
Consult online resources or textbooks for clarification on challenging topics.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Gain a solid foundation in deep learning principles, which are essential for understanding modern speaker recognition systems.

View Deep Learning on Amazon

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Focus on the chapters related to convolutional neural networks, recurrent neural networks, and autoencoders.
Work through the exercises at the end of each chapter to test your understanding.
Explore the online resources and code examples provided by the authors.

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Read 'Fundamentals of Speech Recognition' by Rabiner and Juang

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Gain a deeper understanding of the theoretical underpinnings of speaker recognition by studying a classic text on speech recognition.

View Fundamentals of Speech Recognition on Amazon

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Read the chapters related to feature extraction and acoustic modeling.
Take notes on key concepts and algorithms.
Compare and contrast the approaches described in the book with those presented in the course.

Read 'Fundamentals of Speech Recognition' by Rabiner and Juang

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Gain a deeper understanding of the theoretical underpinnings of speech and speaker recognition, including feature extraction and acoustic modeling.

View Fundamentals of Speech Recognition on Amazon

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Read the chapters related to feature extraction and acoustic modeling.
Take notes on key concepts and formulas.
Attempt the exercises at the end of each chapter to test your understanding.

Create a Blog Post on Deep Learning for Speaker Recognition

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Deepen your understanding of deep learning techniques in speaker recognition by writing a blog post that explains the concepts in a clear and concise manner.

Browse courses on Deep Learning

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Research different deep learning architectures used in speaker recognition, such as CNNs, RNNs, and Transformers.
Write a blog post that explains the advantages and disadvantages of each architecture.
Include code examples and visualizations to illustrate the concepts.

Implement Cosine Similarity in Python

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Solidify your understanding of similarity scoring by implementing cosine similarity, a fundamental technique used in speaker recognition.

Browse courses on Cosine Similarity

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Write a Python function that takes two vectors as input and returns their cosine similarity.
Test your function with sample data to ensure it produces correct results.
Apply your function to speaker embeddings generated from audio data.

Implement MFCC Feature Extraction in Python

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Solidify your understanding of MFCC feature extraction by implementing it from scratch using Python, reinforcing your coding skills and knowledge of audio processing.

Browse courses on Feature Extraction

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Research the MFCC algorithm and its various steps.
Implement each step of the algorithm in Python, such as pre-emphasis, framing, windowing, FFT, Mel filterbank, and DCT.
Test your implementation with sample audio files and compare the results with existing libraries.

Create a Blog Post on Speaker Recognition Applications

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Reinforce your understanding of speaker recognition by researching and writing about its various applications in different industries.

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Research different applications of speaker recognition, such as security, authentication, and healthcare.
Write a blog post summarizing your findings, including examples and use cases.
Publish your blog post on a platform like Medium or LinkedIn.

Build a Simple Speaker Verification System

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Apply the knowledge gained from the course to build a functional speaker verification system, integrating audio processing, feature extraction, and machine learning techniques.

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Collect or find a suitable dataset of speech samples from different speakers.
Implement feature extraction techniques, such as MFCC or i-vectors.
Train a machine learning model, such as a Gaussian Mixture Model (GMM) or a deep neural network, to model speaker characteristics.
Evaluate the performance of your system using appropriate metrics, such as Equal Error Rate (EER).

Contribute to an Open-Source Speaker Recognition Project

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Enhance your practical skills and contribute to the community by participating in an open-source speaker recognition project, gaining experience in collaborative development and real-world applications.

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Find an open-source speaker recognition project on platforms like GitHub.
Explore the project's codebase and documentation to understand its structure and functionality.
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Submit your contributions through pull requests and engage with the project's community.

Career center

Learners who complete Speaker Recognition | By Award Winning Textbook Author will develop knowledge and skills that may be useful to these careers:

Voice Technology Specialist

A voice technology specialist works with various aspects of voice-based technologies, and this course would provide essential training in speaker recognition techniques. This course covers basic concepts and core algorithms in speaker recognition, which any voice specialist needs. This course also provides knowledge about audio signals, acoustics, and signal processing. A voice technology specialist would greatly benefit from the deep learning portion of the course, as well as the hands-on projects. This course directly prepares a technology specialist for real-world technical challenges.

See salaries and explore the career path for Voice Technology Specialist

Biometrics Engineer

The role of a biometrics engineer includes developing and implementing biometric recognition systems, and this course offers important training in the specific area of speaker recognition. This career demands a strong understanding of signal processing, machine learning, and various recognition algorithms, which are all core elements of this course. The hands-on coding experience and the final project will be especially valuable for a biometrics engineer, allowing them to move from abstract theory to concrete, practical implementation. This course emphasizes practical system building, offering experience directly relevant to the daily tasks of a biometrics engineer.

See salaries and explore the career path for Biometrics Engineer

Artificial Intelligence Specialist

An artificial intelligence specialist focuses on developing intelligent systems, and this course offers a strong foundation in a specific subset of AI -- speaker recognition. This career requires knowledge of machine learning, deep learning models, and audio processing, all of which are covered in detail by this course. An AI specialist would value the hands-on practices in machine learning and the deep learning component of the course, allowing them to move from concepts to real implementable code. The course emphasizes modern speaker recognition, which is vital for experts in the field.

See salaries and explore the career path for Artificial Intelligence Specialist

Machine Learning Engineer

A machine learning engineer develops and implements machine learning models, and this course provides a strong foundation in building such systems for speaker recognition. This role often requires deep knowledge of machine learning algorithms, audio processing, and signal processing, all which are covered in the course. The hands-on coding experience will help a machine learning engineer to effectively translate research into practical applications. Additionally, the course teaches various machine learning models, such as Gaussian mixture models and neural networks, which are vital to machine learning engineering.

See salaries and explore the career path for Machine Learning Engineer

Signal Processing Engineer

A signal processing engineer works with the analysis, modification, and synthesis of signals, and this course provides detailed instruction on signal processing, specifically as applied to speaker recognition. This course dives into signal processing techniques such as time and frequency domain analysis as related to audio signals. A signal processing engineer will find great value in the practical application of these techniques as applied to feature extraction. This course will help the engineer understand the mechanics of signal processing in speaker recognition.

See salaries and explore the career path for Signal Processing Engineer

Acoustics Engineer

An acoustics engineer works primarily with sound and its behavior, and this course provides a rich introduction into the fundamentals of acoustics and audio processing. A core part of the acoustics engineering role is to understand how to design, build and test acoustic systems, with a detailed knowledge of the properties of sound itself. This course covers audio signal processing, feature extraction, and the use of various software tools for audio analysis which is useful for an acoustics engineer. Such an engineer needs to understand the properties of sound, and the course covers many of the essentials.

See salaries and explore the career path for Acoustics Engineer

Data Scientist

Data scientists analyze data to extract insights, and this course provides an understanding of how to work with audio data in the context of speaker recognition. The course provides useful training in machine learning techniques relevant to audio data, such as Gaussian mixture models, support vector machines, and neural networks. Additionally, this course discusses data processing and techniques like data cleansing and augmentation, which are crucial skills for a data scientist who deals with diverse data sets. The hands-on coding projects will directly translate to real world work.

See salaries and explore the career path for Data Scientist

Audio Engineer

An audio engineer works with the technical aspects of sound, and this course provides useful knowledge in audio processing and acoustics. Audio engineers need to be familiar with signal processing techniques, and the course covers these concepts in detail. Furthermore, understanding feature extraction and audio formats, as provided in this course, benefits an audio engineer and enables them to perform signal processing at a high level. Gaining an understanding of audio analysis using tools like Audacity and SoX will be useful.

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Speech Scientist

A speech scientist researches speech and audio processing, and this course may be useful to help build a foundation in speaker recognition techniques. The course covers fundamentals of acoustics, feature extraction, and machine learning models, all of which are crucial for advancing this field. A speech scientist would also benefit from the course’s focus on modern deep learning-based systems in speaker recognition. The historical perspective on the evolution of speaker recognition, from early techniques to modern deep learning, is also useful for speech scientists who need this background to conduct advanced research.

See salaries and explore the career path for Speech Scientist

Research Scientist

A research scientist investigates scientific questions, and this course may help form a foundation for those who want to explore speaker recognition. The course covers machine learning and deep learning techniques, providing a deep dive into various algorithms used for speaker identification. A research scientist would find the historical context especially helpful as well as a comprehensive understanding of the field. The course helps in learning how to convert concepts into practical systems, which is a focus during hands-on work.

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System Architect

A system architect designs complex systems, and this course will help to build a foundation in speaker recognition to design these systems. The course work will give the architect an understanding of the technical underpinnings of speaker recognition systems by exploring audio processing, machine learning, and system design. Understanding how to incorporate deep learning models, as taught in this course, is important for large scale system design. The system architect will be able to make informed decisions in their field by understanding the course content.

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Software Developer

A software developer creates applications and systems, and this course may be useful to developers looking to apply speaker recognition technologies. This course teaches the fundamentals of audio processing, machine learning, and practical implementation using Python and PyTorch. Specific training on feature extraction and model building would be especially useful for any software developer planning to implement or integrate speaker recognition into software applications, giving them the tools they need to get started. This course provides hands-on coding examples, preparing a software developer for real projects.

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Product Manager

A product manager defines and guides the development of a product, and this course may be useful for those who wish to manage voice recognition products. While this role may not require you to implement the technology, the course will help you understand the basic concepts of audio processing, machine learning, as well as the history of speaker recognition. This course can help a product manager understand the technologies they would manage. A product manager needs a high-level perspective on the technical challenges involved, and this course will help a product manager communicate with their development team.

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Bioacoustician

A bioacoustician is a scientist who studies animal sounds, and this course may be useful to understand sound and audio processing. The course covers the fundamentals of acoustics, audio processing and signal analysis. A bioacoustician can apply these techniques to analyze and understand animal vocalizations. The course provides an opportunity to learn signal processing techniques relevant to biological sounds. Although the primary focus is speaker recognition of humans, a bioacoustician would likely find value in the audio signal fundamentals of this course.

See salaries and explore the career path for Bioacoustician

Voice User Interface Designer

A voice user interface designer creates voice experiences, and this course may help in understanding the technical aspects of voice recognition. The course covers the basics of acoustics, signal processing, and machine learning, which helps a user interface designer understand how the technology functions. A voice user interface designer may better design user centered voice experiences by gaining an understanding of the limitations and possibilities of the underlying technology. This course will help provide more context to their work.

See salaries and explore the career path for Voice User Interface Designer

By Award Winning Textbook Author

What's inside

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Reviews summary

Speaker recognition: from basics to deep learning

Activities

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