Speech Recognition Based Upon a Segment Classification and Labelling Technique and Hidden Markov Model

Auteurs-es

  • W. A. Mahmoud
  • L. A. M. Bennett

Résumé

A new structure for isolated-word speech recognition via vector quantisation (VQ) is described, namely the segment classification and labelling technique (SCLT). The proposed recognizer requires the generation of separate codebooks for the acoustically dissimilar events and then the merging of them to produce a single reference codebook. Three major acoustic events were considered, namely voiced, unvoiced and silence (V/U/S). The results show that the proposed structure has the capability of reducing the degradation of VQ in speech recognition and provides a better set of observations for the hidden Markov model (HMM).

Fichiers supplémentaires

Publié-e

2022-12-03

Comment citer

1.
Mahmoud WA, Bennett LAM. Speech Recognition Based Upon a Segment Classification and Labelling Technique and Hidden Markov Model. Canadian Acoustics [Internet]. 3 déc. 2022 [cité 12 sept. 2024];14(3 bis):91-2. Disponible à: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/3535

Numéro

Vol. 14 No. 3 bis (1986): Symposium sur la reconnaissance de la parole

Rubrique

Actes du congrès de la Semaine canadienne d'acoustique

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