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Computer Science

D-Index
35
Citations
5915
World Ranking
11612
National Ranking
731

Overview

Thomas Hain is affiliated with the University of Sheffield in the United Kingdom. Their research primarily focuses on areas within computer science, specifically in artificial intelligence and signal processing. They have contributed extensively to subfields including experimental and cognitive psychology, computational mechanics, and cognitive neuroscience.

The main topics covered in Thomas Hain's work include speech recognition and synthesis, speech and audio processing, and music and audio processing. Additional topics include natural language processing techniques, speech and dialogue systems, phonetics and phonology research, and advanced adaptive filtering techniques.

Frequent coauthors working alongside Thomas Hain include Stefan Goetze, George Close, William Ravenscroft, Mingjie Chen, and Yanpei Shi.

Common venues for Hain's publications are:

  • arXiv (Cornell University)
  • 2022 30th European Signal Processing Conference (EUSIPCO)
  • Interspeech 2022
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Recent papers authored or coauthored by Thomas Hain include:

  • H-VECTORS: Improving the robustness in utterance-level speaker embeddings using a hierarchical attention model, 2021, Neural Networks
  • MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data, 2022, 2022 30th European Signal Processing Conference (EUSIPCO)
  • Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection, 2021, Journal Of Big Data
  • Unsupervised Data Selection for Speech Recognition with Contrastive Loss Ratios, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Att-TasNet: Attending to Encodings in Time-Domain Audio Speech Separation of Noisy, Reverberant Speech Mixtures, 2022, Frontiers in Signal Processing

Best Publications

  • The AMI meeting corpus: a pre-announcement

    Jean Carletta;Simone Ashby;Sebastien Bourban;Mike Flynn

  • The AMI meeting corpus

    I. McCowan;J. Carletta;W. Kraaij;S. Ashby

  • Recognition and understanding of meetings the AMI and AMIDA projects

    S. Renals;T. Hain;H. Bourlard

  • The MGB challenge: Evaluating multi-genre broadcast media recognition

    P Bell;M J F Gales;T Hain;J Kilgour

  • New features in the CU-HTK system for transcription of conversational telephone speech

    T. Hain;P.C. Woodland;G. Evermann;D. Povey

  • The AMI System for the Transcription of Speech in Meetings

    T. Hain;V. Wan;L. Burget;M. Karafiat

  • A Comparative Study of Adaptive, Automatic recognition of Disordered Speech

    Heidi Christensen;Stuart P. Cunningham;Charles Fox;Phil D. Green

  • Segment generation and clustering in the HTK broadcast news transcription system

    T Hain;SE Johnson;A Tuerk;PC Woodland

  • Transcribing Meetings With the AMIDA Systems

    T. Hain;L. Burget;J. Dines;P. N. Garner

  • Hypothesis spaces for minimum Bayes risk training in large vocabulary speech recognition.

    Matthew Gibson;Thomas Hain

  • The 1997 HTK broadcast news transcription system

    PC Woodland;T Hain;SE Johnson;TR Niesler

  • The 1998 HTK system for transcription of conversational telephone speech

    T. Hain;P.C. Woodland;T.R. Niesler;E.W.D. Whittaker

  • Using neural network front-ends on far field multiple microphones based speech recognition

    Yulan Liu;Pengyuan Zhang;Thomas Hain

  • The segmentation of multi-channel meeting recordings for automatic speech recognition

    John Dines;Jithendra Vepa;Thomas Hain

  • Juicer : A weighted finite-state transducer speech decoder

    Darren Moore;John Dines;Mathew Magimai Doss;Jithendra Vepa

  • The AMI meeting transcription system: progress and performance

    Thomas Hain;Lukas Burget;John Dines;Giulia Garau

  • Development of the 2003 CU-HTK conversational telephone speech transcription system

    G. Evermann;H.Y. Chan;M.J.F. Gales;T. Hain

  • The 2005 AMI system for the transcription of speech in meetings

    Thomas Hain;Lukas Burget;John Dines;Giulia Garau

  • Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on

    Steve Renals;Thomas Hain;Herve Bourlard

  • Implicit modelling of pronunciation variation in automatic speech recognition

    Thomas Hain

  • The development of the AMI system for the transcription of speech in meetings

    Thomas Hain;Lukas Burget;John Dines;Iain McCowan

Frequent Co-Authors

Steve Renals
Steve Renals University of Edinburgh
Martin Karafiat
Martin Karafiat Brno University of Technology
Philip C. Woodland
Philip C. Woodland University of Cambridge
Lukas Burget
Lukas Burget Brno University of Technology
Jon Barker
Jon Barker University of Sheffield
Lucia Specia
Lucia Specia Imperial College London
Mark J. F. Gales
Mark J. F. Gales University of Cambridge
Daniel Povey
Daniel Povey Xiaomi (China)
Hervé Bourlard
Hervé Bourlard Idiap Research Institute
Iain A. McCowan
Iain A. McCowan Queensland University of Technology

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