Speaker Recognition | By Award Winning Textbook Author

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 And most technical books were already outdated when they were published.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.