Tara Sainath
Tara N. Sainath is an American computer scientist whose research involves deep learning applied to speech recognition. She is a principal research scientist at Google Research.
Education and career
[edit]Sainath was a student of electrical and engineering and computer science at the Massachusetts Institute of Technology, where she received a bachelor's degree, a master's degree in 2005, and a Ph.D. in 2009. Her master's thesis was Acoustic Landmark Detection and Segmentation using the McAulay-Quatieri Sinusoidal Model, supervised by Timothy Hazen,[1] and her doctoral dissertation was Applications of Broad Class Knowledge for Noise Robust Speech Recognition, supervised by Victor Zue.[2][3]
She worked for IBM Research at the Thomas J. Watson Research Center before moving to Google Research.[4]
Recognition
[edit]Sainath was elected both as an IEEE Fellow and as a fellow of the International Speech Communication Association in 2022, in both cases "for contributions to deep learning for automatic speech recognition".[5][6]
References
[edit]- ^ Sainath, Tara N. (2005), Acoustic Landmark Detection and Segmentation using the McAulay-Quatieri Sinusoidal Model (PDF), Massachusetts Institute of Technology, retrieved 2023-04-21
- ^ Sainath, Tara N. (2009), Applications of Broad Class Knowledge for Noise Robust Speech Recognition (PDF), Massachusetts Institute of Technology, retrieved 2023-04-21
- ^ Tara Sainath at the Mathematics Genealogy Project
- ^ Tara Sainath, Google Research, retrieved 2023-04-21
- ^ 2022 Newly Elevated Fellows (PDF), IEEE, archived from the original (PDF) on 2021-11-24, retrieved 2023-04-21
- ^ Wellekens, Chris (May 9, 2022), "ISCA Fellows announced", ISCApad, no. 287, International Speech Communication Association
External links
[edit]- Home page
- Tara Sainath publications indexed by Google Scholar
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