Experiments on Acoustic Model supervised adaptation and evaluation by K-Fold Cross Validation technique. - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue 5th International Symposium on I/V Communications and Mobile Network Année : 2010

Experiments on Acoustic Model supervised adaptation and evaluation by K-Fold Cross Validation technique.

Résumé

This paper is an analysis of adaptation techniques for French acoustic models (hidden Markov models). The LVCSR engine Julius, the Hidden Markov Model Toolkit (HTK) and the K-Fold CV technique are used together to build three different adaptation methods: Maximum Likelihood a priori (ML), Maximum Likelihood Linear Regression (MLLR) and Maximum a posteriori (MAP). Experimental results by means of word and phoneme error rate indicate that the best adaptation method depends on the adaptation data, and that the acoustic models performance can be improved by the use of alignments at phoneme-level and K-Fold Cross Validation (CV). The very known K-Fold CV technique will point to the best adaptation technique to follow considering each case of data type.
Fichier principal
Vignette du fichier
148.pdf (162.39 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00578548 , version 1 (21-03-2011)

Identifiants

Citer

Daniel Régis Sarmento Caon, Asmaa Amehraye, Joseph Razik, Gérard Chollet, Rodrigo V. Andreão, et al.. Experiments on Acoustic Model supervised adaptation and evaluation by K-Fold Cross Validation technique.. 5th International Symposium on I/V Communications and Mobile Network, 2010, pp.148. ⟨10.1109/ISVC.2010.5656264⟩. ⟨hal-00578548⟩
153 Consultations
136 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More