Progress on Phoneme Recognition with a Continuous-State HMM

Phil Weber, Linxue Bai, Stephen Houghton, Peter Jancovic, Martin Russell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Recent advances in automatic speech recognition have used large corpora and powerful computational resources to train complex statistical models from high-dimensional features, to attempt to capture all the variability found in natural speech. Such models are difficult to interpret and may be fragile, and contradict or ignore knowledge of human speech production and perception. We report progress towards phoneme recognition using a model of speech which employs very few parameters and which is more faithful to the dynamics and model of human speech production. Using features generated from a neural network bottleneck layer, we obtain recognition accuracy on TIMIT which compares favourably with traditional models of similar power. We discuss the implications of these results for recognition using natural features such as vocal tract resonances and spectral energies
Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings
PublisherIEEE Xplore
Number of pages5
ISBN (Electronic)2379-190X
Publication statusE-pub ahead of print - 19 May 2016
EventIEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) 2016 - China, Shanghai, China
Duration: 20 Mar 201625 Mar 2016


ConferenceIEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) 2016
Internet address


  • Hidden Markov models
  • speech
  • training
  • video recording
  • speech recognition
  • computational modeling
  • data models


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