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Daniel Povey

Daniel Povey

D-Index & Metrics

Computer Science

D-Index
67
Citations
37874
World Ranking
2136
National Ranking
292

Overview

Daniel Povey is affiliated with Xiaomi (China) and works primarily in China. Their research activity spans across the field of Computer Science, with a particular emphasis on Artificial Intelligence and Signal Processing.

Their recent publications cover topics relevant to speech recognition and audio processing, including:

  • CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, arXiv (Cornell University))
  • Pruned RNN-T for fast, memory-efficient ASR training (2022, Interspeech 2022)
  • Zipformer: A faster and better encoder for automatic speech recognition (2023, arXiv (Cornell University))
  • GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio (2021, arXiv (Cornell University))
  • Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition (2023, IEEE/ACM Transactions on Audio Speech and Language Processing)

The scholar's frequent coauthors include Sanjeev Khudanpur, Zengwei Yao, Wei Kang, Liyong Guo, and Na Li, reflecting a collaborative research environment in related technical fields.

Daniel Povey has contributed notably to venues such as:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • Interspeech 2022
  • IEEE Signal Processing Letters

Book publications appear under Springer Science+Business Media, with multiple editions titled Artificial Intelligence published in 2022.

Their research addresses several topics in speech and audio technology, including:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Topic Modeling
  • Advanced Data Compression Techniques

Best Publications

  • The Kaldi Speech Recognition Toolkit

    Daniel Povey;Arnab Ghoshal;Gilles Boulianne;Lukas Burget

  • Librispeech: An ASR corpus based on public domain audio books

    Vassil Panayotov;Guoguo Chen;Daniel Povey;Sanjeev Khudanpur

  • X-Vectors: Robust DNN Embeddings for Speaker Recognition

    David Snyder;Daniel Garcia-Romero;Gregory Sell;Daniel Povey

  • Audio augmentation for speech recognition.

    Tom Ko;Vijayaditya Peddinti;Daniel Povey;Sanjeev Khudanpur

  • A time delay neural network architecture for efficient modeling of long temporal contexts.

    Vijayaditya Peddinti;Daniel Povey;Sanjeev Khudanpur

  • MUSAN: A Music, Speech, and Noise Corpus.

    David Snyder;Guoguo Chen;Daniel Povey

  • The HTK book version 3.4

    SJ Young;G Evermann;Mjf Gales;D Kershaw

  • Deep Neural Network Embeddings for Text-Independent Speaker Verification.

    David Snyder;Daniel Garcia-Romero;Daniel Povey;Sanjeev Khudanpur

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

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

  • Minimum Phone Error and I-smoothing for improved discriminative training

    D. Povey;P.C. Woodland

  • Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI.

    Daniel Povey;Vijayaditya Peddinti;Daniel Galvez;Pegah Ghahremani

  • Sequence-discriminative training of deep neural networks

    Karel Veselý;Arnab Ghoshal;Lukás Burget;Daniel Povey

  • Strategies for training large scale neural network language models

    Tomas Mikolov;Anoop Deoras;Daniel Povey;Lukas Burget

  • Large scale discriminative training of hidden Markov models for speech recognition

    P.C. Woodland;D. Povey

  • Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks.

    Daniel Povey;Gaofeng Cheng;Yiming Wang;Ke Li

  • Boosted MMI for model and feature-space discriminative training

    D. Povey;D. Kanevsky;B. Kingsbury;B. Ramabhadran

  • Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

    Daniel Povey;Xiaohui Zhang;Sanjeev Khudanpur

  • Deep neural network-based speaker embeddings for end-to-end speaker verification

    David Snyder;Pegah Ghahremani;Daniel Povey;Daniel Garcia-Romero

  • Improving deep neural network acoustic models using generalized maxout networks

    Xiaohui Zhang;Jan Trmal;Daniel Povey;Sanjeev Khudanpur

  • The subspace Gaussian mixture model-A structured model for speech recognition

    Daniel Povey;Lukáš Burget;Mohit Agarwal;Pinar Akyazi

  • fMPE: discriminatively trained features for speech recognition

    D. Povey;B. Kingsbury;L. Mangu;G. Saon

Frequent Co-Authors

Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University
George Saon
George Saon IBM (United States)
Daniel Garcia-Romero
Daniel Garcia-Romero Johns Hopkins University
Brian Kingsbury
Brian Kingsbury IBM (United States)
Hagen Soltau
Hagen Soltau Google (United States)
Lukas Burget
Lukas Burget Brno University of Technology
Philip C. Woodland
Philip C. Woodland University of Cambridge
Martin Karafiat
Martin Karafiat Brno University of Technology
Thomas Hain
Thomas Hain University of Sheffield
Najim Dehak
Najim Dehak Johns Hopkins University

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