World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
41093
World Ranking
1311
National Ranking
692

Overview

Tara N. Sainath is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and signal processing. Their research predominantly centers around speech recognition and synthesis, speech and audio processing, and related areas such as music and audio processing and natural language processing techniques.

They have authored numerous publications including recent papers such as:

  • "Self-Supervised Speech Representation Learning: A Review" (2022, IEEE Journal of Selected Topics in Signal Processing)
  • "BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition" (2022, IEEE Journal of Selected Topics in Signal Processing)
  • "End-to-End Speech Recognition: A Survey" (2023, IEEE/ACM Transactions on Audio Speech and Language Processing)
  • "Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages" (2023, arXiv (Cornell University))
  • "Scaling End-to-End Models for Large-Scale Multilingual ASR" (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU))

Their frequent co-authors include Trevor Strohman, Rohit Prabhavalkar, Shuo-Yiin Chang, Bo Li, and Weiran Wang. These collaborations have supported research spanning large-scale speech recognition models and multilingual applications.

Sainath's work is often published in venues including:

  • arXiv (Cornell University)
  • Interspeech 2022
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2022 IEEE Spoken Language Technology Workshop (SLT)
  • IEEE Journal of Selected Topics in Signal Processing

Their contributions cover several subfields within computer science:

  • Artificial Intelligence
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Experimental and Cognitive Psychology
  • Control and Systems Engineering

Sainath's main research topics include:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Speech and Dialogue Systems
  • Phonetics and Phonology Research

Best Publications

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

    G. Hinton;Li Deng;Dong Yu;G. E. Dahl

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition

    Geoffrey Hinton;Li Deng;Dong Yu;George Dahl

  • Improving deep neural networks for LVCSR using rectified linear units and dropout

    George E. Dahl;Tara N. Sainath;Geoffrey E. Hinton

  • Deep Convolutional Neural Networks for Large-scale Speech Tasks

    Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau

  • Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

    Tara N. Sainath;Oriol Vinyals;Andrew Senior;Hasim Sak

  • Deep convolutional neural networks for LVCSR

    Tara N. Sainath;Abdel-rahman Mohamed;Brian Kingsbury;Bhuvana Ramabhadran

  • State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • Deep Learning for Audio Signal Processing

    Hendrik Purwins;Bo Li;Tuomas Virtanen;Jan Schluter

  • Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets

    Tara N. Sainath;Brian Kingsbury;Vikas Sindhwani;Ebru Arisoy

  • Streaming End-to-end Speech Recognition for Mobile Devices

    Yanzhang He;Tara N. Sainath;Rohit Prabhavalkar;Ian McGraw

  • Convolutional Neural Networks for Small-Footprint Keyword Spotting

    Tara N. Sainath;Carolina Parada

  • Learning the Speech Front-end with Raw Waveform CLDNNs

    Tara N. Sainath;Ron J. Weiss;Andrew W. Senior;Kevin W. Wilson

  • Self-Supervised Speech Representation Learning: A Review

    Unknown

  • Deep Belief Networks using discriminative features for phone recognition

    Abdel-rahman Mohamed;Tara N. Sainath;George Dahl;Bhuvana Ramabhadran

  • A Comparison of Sequence-to-Sequence Models for Speech Recognition

    Rohit Prabhavalkar;Kanishka Rao;Tara N. Sainath;Bo Li

  • Improvements to Deep Convolutional Neural Networks for LVCSR

    Tara N. Sainath;Brian Kingsbury;Abdel-rahman Mohamed;George E. Dahl

  • Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization.

    Brian Kingsbury;Tara N. Sainath;Hagen Soltau

  • Deep Neural Network Language Models

    Ebru Arisoy;Tara N. Sainath;Brian Kingsbury;Bhuvana Ramabhadran

  • Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition

    Tara N. Sainath;Ron J. Weiss;Kevin W. Wilson;Bo Li

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

    Anjuli Kannan;Yonghui Wu;Patrick Nguyen;Tara N. Sainath

  • The shared views of four research groups )

    Geoffrey Hinton;Li Deng;Dong Yu;George E. Dahl

Frequent Co-Authors

Dimitri Kanevsky
Dimitri Kanevsky Google (United States)
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Yonghui Wu
Yonghui Wu Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Brian Kingsbury
Brian Kingsbury IBM (United States)
Michiel Bacchiani
Michiel Bacchiani Google (United States)
David Nahamoo
David Nahamoo Pyron Inc.

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