Hybrid language models for speech transcription - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Hybrid language models for speech transcription

Denis Jouvet

Résumé

This paper analyzes the use of hybrid language models for automatic speech transcription. The goal is to later use such an approach as a support for helping communication with deaf people, and to run it on an embedded decoder on a portable device, which introduces constraints on the model size. The main linguistic units considered for this task are the words and the syllables. Various lexicon sizes are studied by setting thresholds on the word occurrence frequencies in the training data, the less frequent words being therefore syllabified. A recognizer using this kind of language model can output between 62% and 96% of words (with respect to the thresholds on the word occurrence frequencies; the other recognized lexical units are syllables). By setting different thresholds on the confidence measures associated to the recognized words, the most reliable word hypotheses can be identified, and they have correct recognition rates between 70% and 92%.
Fichier principal
Vignette du fichier
articleIS2014-LM-version du 20 juin.pdf (153.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01090478 , version 1 (03-12-2014)

Identifiants

  • HAL Id : hal-01090478 , version 1

Citer

Luiza Orosanu, Denis Jouvet. Hybrid language models for speech transcription. INTERSPEECH 2014, 15th Annual Conference of the International Speech Communication Association, Sep 2014, Singapour, Singapore. ⟨hal-01090478⟩
159 Consultations
294 Téléchargements

Partager

Gmail Facebook X LinkedIn More