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Computer Science

D-Index
46
Citations
12847
World Ranking
6702
National Ranking
2959

Overview

Michael L. Seltzer is affiliated with Facebook in the United States and has contributed to research primarily within the field of Computer Science. Their scholarly work spans several subfields, including Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Mechanical Engineering, and Electrical and Electronic Engineering.

The main topics that Michael L. Seltzer has focused on are diverse and include Speech Recognition and Synthesis, Natural Language Processing Techniques, Topic Modeling, Speech and Dialogue Systems, Speech and Audio Processing, Music and Audio Processing, and Control Systems in Engineering.

Michael L. Seltzer has published extensively, with a significant number of papers featured in well-known venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Interspeech 2022
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • The Journal of the Acoustical Society of America
  • Encyclopedia of Social Work

Some of the recent publications are:

  • "Streaming parallel transducer beam search with fast slow cascaded encoders", 2022, Interspeech 2022
  • "Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric", 2022, Interspeech 2022
  • "Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding", 2021, arXiv (Cornell University)
  • "Deliberation Model for On-Device Spoken Language Understanding", 2022, Interspeech 2022
  • "Weak-Attention Suppression For Transformer Based Speech Recognition", 2020, arXiv (Cornell University)

Throughout their career, Michael L. Seltzer has collaborated frequently with several coauthors including:

  • Ozlem Kalinli
  • Christian Fuegen
  • Duc Le
  • Jay Mahadeokar
  • Yangyang Shi

Best Publications

  • Recent advances in deep learning for speech research at Microsoft

    Li Deng;Jinyu Li;Jui-Ting Huang;Kaisheng Yao

  • A study on data augmentation of reverberant speech for robust speech recognition

    Tom Ko;Vijayaditya Peddinti;Daniel Povey;Michael L. Seltzer

  • An investigation of deep neural networks for noise robust speech recognition

    Michael L. Seltzer;Dong Yu;Yongqiang Wang

  • Achieving Human Parity in Conversational Speech Recognition

    Wayne Xiong;Jasha Droppo;Xuedong Huang;Frank Seide

  • Binary Coding of Speech Spectrograms Using a Deep Auto-encoder

    Li Deng;Michael L. Seltzer;Dong Yu;Alex Acero

  • An Introduction to Computational Networks and the Computational Network Toolkit

    Dong Yu;Adam Eversole;Mike Seltzer;Kaisheng Yao

  • Improved Bottleneck Features Using Pretrained Deep Neural Networks.

    Dong Yu;Michael L. Seltzer

  • Multi-task learning in deep neural networks for improved phoneme recognition

    Michael L. Seltzer;Jasha Droppo

  • Reconstruction of missing features for robust speech recognition

    Bhiksha Raj;Michael L. Seltzer;Richard M. Stern

  • The microsoft 2016 conversational speech recognition system

    W. Xiong;J. Droppo;X. Huang;F. Seide

  • CROWDMOS: An approach for crowdsourcing mean opinion score studies

    Flavio Ribeiro;Dinei Florencio;Cha Zhang;Michael Seltzer

  • Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks

    Dong Yu;Michael L. Seltzer;Jinyu Li;Jui-Ting Huang

  • Toward Human Parity in Conversational Speech Recognition

    Wayne Xiong;Jasha Droppo;Xuedong Huang;Frank Seide

  • Transformer-Based Acoustic Modeling for Hybrid Speech Recognition

    Yongqiang Wang;Abdelrahman Mohamed;Due Le;Chunxi Liu

  • A Bayesian Classifier for Spectrographic Mask Estimation for Missing Feature Speech Recognition

    Michael L. Seltzer;Bhiksha Raj;Richard M. Stern

  • Deep beamforming networks for multi-channel speech recognition

    Xiong Xiao;Shinji Watanabe;Hakan Erdogan;Liang Lu

  • Singular value decomposition based low-footprint speaker adaptation and personalization for deep neural network

    Jian Xue;Jinyu Li;Dong Yu;Mike Seltzer

  • Likelihood-maximizing beamforming for robust hands-free speech recognition

    M.L. Seltzer;B. Raj;R.M. Stern

  • Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques

    Reinhold Haeb-Umbach;Shinji Watanabe;Tomohiro Nakatani;Michiel Bacchiani

  • Deep neural network features and semi-supervised training for low resource speech recognition

    Samuel Thomas;Michael L. Seltzer;Kenneth Church;Hynek Hermansky

  • A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition

    Aren Jansen;Emmanuel Dupoux;Sharon Goldwater;Mark Johnson

Frequent Co-Authors

Ivan Tashev
Ivan Tashev Microsoft (United States)
Dong Yu
Dong Yu Tencent (China)
Alejandro Acero
Alejandro Acero Apple (United States)
John Krumm
John Krumm Microsoft (United States)
Jasha Droppo
Jasha Droppo Amazon (United States)
Bhiksha Raj
Bhiksha Raj Carnegie Mellon University
Richard M. Stern
Richard M. Stern Carnegie Mellon University
Jinyu Li
Jinyu Li Microsoft (United States)
Yifan Gong
Yifan Gong Microsoft (United States)
Frank Seide
Frank Seide Microsoft (United States)

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