Tree-based state tying for high accuracy acoustic modelling

  • Authors:
  • S. J. Young;J. J. Odell;P. C. Woodland

  • Affiliations:
  • Cambridge University, Cambridge, England;Cambridge University, Cambridge, England;Cambridge University, Cambridge, England

  • Venue:
  • HLT '94 Proceedings of the workshop on Human Language Technology
  • Year:
  • 1994

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Abstract

The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance between model complexity and available training data. For large vocabulary systems requiring cross-word context dependent modelling, this is particularly acute since many such contexts will never occur in the training data. This paper describes a method of creating a tied-state continuous speech recognition system using a phonetic decision tree. This tree-based clustering is shown to lead to similar recognition performance to that obtained using an earlier data-driven approach but to have the additional advantage of providing a mapping for unseen triphones. State-tying is also compared with traditional model-based tying and shown to be clearly superior. Experimental results are presented for both the Resource Management and Wall Street Journal tasks.