CONTINUOUS SPEECH RECOGNITION BY MEANS OF ACOUSTIC/PHONETIC CLASSIFICATION OBTAINED FROM A HIDDEN MARKOV MODEL.

Research output: Contribution to journalConference articlepeer-review

Abstract

An experimental continuous speech recognition system comprising procedures for acoustic/phonetic classification, lexical access, and sentence retrieval is described. Speech is assumed to be composed of a small number of phonetic units which may be identified with the states of a hidden Markov model. The acoustic correlates of the phonetic units are then characterized by the observable Gaussian process associated with the corresponding state of the underlying Markov chain. Once the parameters of such a model are determined, a phonetic transcription of an utterance can be obtained by means of a Viterbi-like algorithm. In an experimental evaluation of the system, the parameters of an acoustic/phonetic model were estimated from fluent utterances of 37 seven-digit numbers. A digit-recognition rate of 96% was then observed on an independent test set of 59 utterances of the same form from the same speaker.

Original languageEnglish (US)
Pages (from-to)93-96
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Jan 1 1987
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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