Tara Sainath
Distinguished Research Scientist @Google DeepMind, Lead of Gemini Audio Area and Modeling Lead for Project Astra
Dr. Tara Sainath holds an S.B., M.Eng, and PhD in Electrical Engineering and Computer Science from MIT. Her expertise in speech recognition and deep neural networks led to a 5 year stint at IBM T.J. Watson Research Center, and currently fuels her work as a Tech Lead in Google DeepMind's Audio Pillar. There, she focuses on the integration of audio capabilities with large language models (LLMs).
Her technical prowess is recognized through her IEEE Fellowship and awards such as the 2021 IEEE SPS Industrial Innovation Award and co-recipient of the 2022 IEEE SPS Signal Processing Magazine Best Paper Award. 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. Dr. Sainath's leadership is exemplified by her roles as Program Chair for ICLR (2017, 2018) and her extensive work co-organizing influential conferences and workshops, including: Interspeech (2010, 2016, 2019), ICML (2013, 2017), and NeurIPS 2020. Her primary research interests are in deep neural networks for speech recognition.
Audio Processing with Large Language Models
Large Language Models (LLMs) have recently introduced a paradigm shift in Machine Learning, including in audio processing tasks. In automatic speech recognition, we have improved understanding quality across a large number of languages, trained within one universal model.
