World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
70
Citations
26833
World Ranking
1836
National Ranking
104

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2013 - IEEE Fellow For contributions to large vocabulary speech recognition

Overview

Philip C. Woodland is a researcher affiliated with the University of Cambridge in the United Kingdom. Their work primarily centers around computer science, with a strong focus on artificial intelligence and signal processing. Other subfields include computer vision and pattern recognition, experimental and cognitive psychology, and cognitive neuroscience.

Their research covers several topics related to speech and audio technologies, natural language processing, and dialogue systems. Key areas of work include:

  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Speech and Audio Processing
  • Topic Modeling
  • Speech and Dialogue Systems
  • Sentiment Analysis and Opinion Mining

Philip C. Woodland has contributed significantly to scientific literature with numerous publications. Selected recent papers include:

  • "Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition" (2021), published at the 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • "Tree-Constrained Pointer Generator for End-to-End Contextual Speech Recognition" (2021), presented at the 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • "Combination of deep speaker embeddings for diarisation" (2021), published in Neural Networks
  • "Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-Trained Models" (2022), presented at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription" (2022), presented at Interspeech 2022

The most frequent co-authors in Woodland's body of work include:

  • Chao Zhang
  • Guangzhi Sun
  • Qiujia Li
  • Keqi Deng

The researcher has published often in venues such as arXiv (Cornell University), IEEE/ACM Transactions on Audio Speech and Language Processing, the ASRU workshop, ICASSP, and Interspeech conferences.

In 2013, Philip C. Woodland was named an IEEE Fellow for contributions to large vocabulary speech recognition.

Best Publications

  • The HTK book

    SJ Young;J Jansen;JJ Odell;DG Ollason

  • Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

    C. J. Leggetter;Philip C. Woodland

  • Tree-Based State Tying for High Accuracy Modelling

    Steve J. Young;J. J. Odell;Philip C. Woodland

  • Tree-based state tying for high accuracy acoustic modelling

    S. J. Young;J. J. Odell;P. C. Woodland

  • The HTK book version 3.4

    SJ Young;G Evermann;Mjf Gales;D Kershaw

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

    D. Povey;P.C. Woodland

  • Mean and variance adaptation within the MLLR framework

    Mark J. F. Gales;Philip C. Woodland

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

    P.C. Woodland;D. Povey

  • Large vocabulary continuous speech recognition using HTK

    P.C. Woodland;J.J. Odell;V. Valtchev;S.J. Young

  • MMIE training of large vocabulary recognition systems

    V. Valtchev;J. J. Odell;P. C. Woodland;S. J. Young

  • A variable-length category-based n-gram language model

    T.R. Niesler;P.C. Woodland

  • The 1994 HTK large vocabulary speech recognition system

    P.C. Woodland;C.J. Leggetter;J.J. Odell;V. Valtchev

  • A one pass decoder design for large vocabulary recognition

    J. J. Odell;V. Valtchev;P. C. Woodland;S. J. Young

  • A computational model of the auditory periphery for speech and hearing research. I. Ascending path

    Christian Giguère;Philip C. Woodland

  • State clustering in hidden Markov model-based continuous speech recognition

    Steve J. Young;Philip C. Woodland

  • Speaker adaptation of continuous density HMMs using multivariate linear regression

    C. J. Leggetter;Philip C. Woodland

  • The use of state tying in continuous speech recognition.

    Steve J. Young;Philip C. Woodland

  • Large vocabulary decoding and confidence estimation using word posterior probabilities

    G. Evermann;P.C. Woodland

  • Speaker adaptation of HMMs using linear regression

    CJ Leggetter;PC Woodland

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

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

Frequent Co-Authors

Mark J. F. Gales
Mark J. F. Gales University of Cambridge
Steve Young
Steve Young University of Cambridge
Thomas Hain
Thomas Hain University of Sheffield
Yanmin Qian
Yanmin Qian Shanghai Jiao Tong University
Daniel Povey
Daniel Povey Xiaomi (China)
Kai Yu
Kai Yu Shanghai Jiao Tong University
Karen Sparck Jones
Karen Sparck Jones University of Cambridge
William Byrne
William Byrne University of Cambridge
Steve Renals
Steve Renals University of Edinburgh
William D. Marslen-Wilson
William D. Marslen-Wilson University of Cambridge

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