End-to-End Speech Recognition: The Journey from Research to Production

Abstract: 

End-to-end (E2E) speech recognition has become a popular research paradigm in recent years, allowing the modular components of a conventional speech recognition system (acoustic model, pronunciation model, language model), to be replaced by one neural network. In this talk, we will discuss a multi-year research journey of E2E modeling for speech recognition at Google. This journey has resulted in E2E models that can surpass the performance of conventional models across many different quality and latency metrics, as well as the productionization of E2E models for Pixel 4, 5 and 6 phones. We will also touch upon future research efforts with E2E models, including multi-lingual speech recognition.

Bio: 

Tara Sainath received her S.B., M.Eng and PhD in Electrical Engineering and Computer Science (EECS) from MIT. After her PhD, she spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. She has served as a Program Chair for ICLR in 2017 and 2018. Also, she has co-organized numerous special sessions and workshops for many speech and machine learning conferences. In addition, she has served as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) as well as the Associate Editor for IEEE/ACM Transactions on Audio, Speech, and Language Processing. She is an IEEE and ISCA Fellow. In addition, she is the recipient of the 2021 IEEE SPS Industrial Innovation Award as well as the 2022 IEEE SPS Signal Processing Magazine Best Paper Award. She is currently a Principal Research Scientist at Google, working on applications of deep neural networks for automatic speech recognition.

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