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Computer Science

D-Index
31
Citations
4506
World Ranking
13596
National Ranking
5429

Overview

Rohit Prabhavalkar is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a focus on speech and audio technologies. Their research spans core areas in artificial intelligence, signal processing, and applications of machine learning to speech recognition and synthesis.

The scientist's publication record includes works primarily centered on speech recognition systems, audio processing, and related computational techniques. Their recent papers reflect ongoing developments in automatic speech recognition (ASR), end-to-end neural models, and large-scale multilingual approaches. Notable recent publications include:

  • 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)
  • E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR, 2022, Interspeech 2022
  • JOIST: A Joint Speech and Text Streaming Model for ASR, 2023, 2022 IEEE Spoken Language Technology Workshop (SLT)
  • Improving The Latency And Quality Of Cascaded Encoders, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Co-authorship indicates collaboration with several researchers frequently working in related fields, including:

  • Tara N. Sainath
  • Trevor Strohman
  • Weiran Wang
  • Cal Peyser
  • Zhong Meng

Most publications have appeared in venues specializing in speech and language processing, machine learning, and signal processing, reflecting the interdisciplinary nature of the research. These venues include:

  • arXiv (Cornell University)
  • Interspeech 2022
  • 2022 IEEE Spoken Language Technology Workshop (SLT)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • IEEE/ACM Transactions on Audio Speech and Language Processing

Within the broad field of computer science, Rohit Prabhavalkar's research is concentrated in the following subfields:

  • Artificial Intelligence
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Statistical and Nonlinear Physics

Their main topics of work reflect a comprehensive engagement with speech and audio technologies and include:

  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Speech and Audio Processing
  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and Dialogue Systems
  • Fault Detection and Control Systems

Best Publications

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

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

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

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

  • Exploring architectures, data and units for streaming end-to-end speech recognition with RNN-transducer

    Kanishka Rao;Hasim Sak;Rohit Prabhavalkar

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

    Rohit Prabhavalkar;Kanishka Rao;Tara N. Sainath;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

  • Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition

    Chris Donahue;Bo Li;Rohit Prabhavalkar

  • Personalized speech recognition on mobile devices

    Ian McGraw;Rohit Prabhavalkar;Raziel Alvarez;Montse Gonzalez Arenas

  • A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

    Unknown

  • End-to-End Speech Recognition: A Survey

    Unknown

  • Minimum Word Error Rate Training for Attention-Based Sequence-to-Sequence Models

    Rohit Prabhavalkar;Tara N. Sainath;Yonghui Wu;Patrick Nguyen

  • Deep Context: End-to-end Contextual Speech Recognition

    Golan Pundak;Tara N. Sainath;Rohit Prabhavalkar;Anjuli Kannan

  • From Audio to Semantics: Approaches to End-to-End Spoken Language Understanding

    Parisa Haghani;Arun Narayanan;Michiel Bacchiani;Galen Chuang

  • Two-Pass End-to-End Speech Recognition

    Sainath Tara C;Pang Ruoming;Rybach David;He Yanzhang

  • On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition

    Rohit Prabhavalkar;Ouais Alsharif;Antoine Bruguier;Lan McGraw

  • Recognizing Long-Form Speech Using Streaming End-to-End Models

    Arun Narayanan;Rohit Prabhavalkar;Chung-Cheng Chiu;David Rybach

  • Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks

    Rohit Prabhavalkar;Raziel Alvarez;Carolina Parada;Preetum Nakkiran

  • Compressing Deep Neural Networks using a Rank-Constrained Topology

    Preetum Nakkiran;Raziel Alvarez;Rohit Prabhavalkar;Carolina Parada

  • On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition

    Rohit Prabhavalkar;Ouais Alsharif;Antoine Bruguier;Ian McGraw

  • Streaming small-footprint keyword spotting using sequence-to-sequence models

    Yanzhang He;Rohit Prabhavalkar;Kanishka Rao;Wei Li

  • State-of-the-art Speech Recognition With Sequence-to-Sequence Models

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

  • A Comparison of End-to-End Models for Long-Form Speech Recognition

    Chung-Cheng Chiu;Anjuli Kannan;Rohit Prabhavalkar;Zhifeng Chen

  • Deliberation Model Based Two-Pass End-To-End Speech Recognition

    Ke Hu;Tara N. Sainath;Ruoming Pang;Rohit Prabhavalkar

  • Improving the Performance of Online Neural Transducer Models

    Tara N. Sainath;Chung-Cheng Chiu;Rohit Prabhavalkar;Anjuli Kannan

  • Phoebe: Pronunciation-aware Contextualization for End-to-end Speech Recognition

    Antoine Bruguier;Rohit Prabhavalkar;Golan Pundak;Tara N. Sainath

  • A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

Frequent Co-Authors

Tara N. Sainath
Tara N. Sainath Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Yonghui Wu
Yonghui Wu Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Zhifeng Chen
Zhifeng Chen Google (United States)
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Michiel Bacchiani
Michiel Bacchiani Google (United States)
Liangliang Cao
Liangliang Cao Google (United States)
Karen Livescu
Karen Livescu Toyota Technological Institute at Chicago

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