• DocumentCode
    312214
  • Title

    Smoothed local adaptation of connectionist systems

  • Author

    Waterhouse, Steve ; Kershaw, Dan ; Robinson, Tony

  • Author_Institution
    Dept. of Eng., Cambridge Univ., UK
  • Volume
    3
  • fYear
    1996
  • fDate
    3-6 Oct 1996
  • Firstpage
    1313
  • Abstract
    ABBOT is the hybrid connectionist hidden Markov model (HMM) large vocabulary continuous speech recognition system developed at Cambridge University Engineering Department. ABBOT makes effective use of the linear input network (LIN) adaptation technique to achieve speaker and channel adaptation. Although the LIN is effective at adapting to new speakers or a new environment (e.g. a different microphone), the transform is global over the input space. In this paper we describe a technique by which the transform may be made locally linear over different regions of the input space. The local linear transforms are combined by an additional network using a non-linear transform. This scheme falls naturally into the mixtures of experts framework
  • Keywords
    hidden Markov models; recurrent neural nets; speech recognition; transforms; vocabulary; ABBOT; channel adaptation; connectionist systems; hybrid connectionist hidden Markov model; input space; large vocabulary continuous speech recognition; linear input network adaptation technique; local linear transforms; microphone; mixtures of experts framework; nonlinear transform; recurrent neural network; smoothed local adaptation; speaker adaptation; transform; Error analysis; Hidden Markov models; Intelligent networks; Loudspeakers; Maximum likelihood linear regression; Microphones; Recurrent neural networks; Speech recognition; Vectors; Vocabulary;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on
  • Conference_Location
    Philadelphia, PA
  • Print_ISBN
    0-7803-3555-4
  • Type

    conf

  • DOI
    10.1109/ICSLP.1996.607854
  • Filename
    607854