Phonological Knowledge Guided HMM State Mapping for Cross-Lingual Speaker Adaptation

John David Scott Dines, Hui Liang
2011
Article de conférence

Within the HMM state mapping-based cross-lingual speaker adaptation framework, the minimum Kullback-Leibler divergence criterion has been typically employed to measure the similarity of two average voice state distributions from two respective languages for state mapping construction. Considering that this simple criterion doesn't take any language-specific information into account, we propose a data-driven, phonological knowledge guided approach to strengthen the mapping construction -- state distributions from the two languages are clustered according to broad phonetic categories using decision trees and mapping rules are constructed only within each of the clusters. Objective evaluation of our proposed approach demonstrates reduction of mel-cepstral distortion and that mapping rules derived from a single training speaker generalize to other speakers, with subtle improvement being detected during subjective listening tests.

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

John David Scott Dines, Hui Liang
2011
Article de conférence

Résumé

Official source

https://infoscience.epfl.ch/record/167431?ln=fr

À propos de ce résultat

Concepts associés (8)

Concepts associés

Chargement

Publications associées (2)

Publications associées

Chargement

Phonological Knowledge Guided HMM State Mapping for Cross-Lingual Speaker Adaptation

John David Scott Dines, Hui Liang

Idiap2011

Chargement

Enhancing State Mapping-Based Cross-Lingual Speaker Adaptation using Phonological Knowledge in a Data-Driven Manner

John David Scott Dines, Hui Liang

HMM state mapping with the Kullback-Leibler divergence as a distribution similarity measure is a simple and effective technique that enables cross-lingual speaker adaptation for speech synthesis. However, since this technique does not take any other potentially useful information into account for mapping construction, an approach involving phonological knowledge in a data-driven manner is proposed in order to produce better state mapping rules – state distributions from the input and output languages are clustered according to broad phonetic categories using a decision tree, and mapping rules are constructed only within each resultant leaf node. Apart from this, previous research shows that a regression class tree that follows the decision tree structure for state tying is detrimental to cross-lingual speaker adaptation. Thus it is also proposed to apply this new approach to regression class tree growth – state distributions from the output language are clustered according to broad phonetic categories using a decision tree, which is then directly used as a regression class tree for transform estimation. Experimental results show that the proposed approach can reduce mel-cepstral distortion consistently and produce state mapping rules and regression class trees that generalize to unseen test speakers. The impacts of the phonological/acoustic similarity between input and output languages upon the reliability of state mapping rules and upon the structure of regression class trees are also demonstrated and analyzed.

Idiap2013

Chargement

Reconnaissance automatique de la parole

vignette|droite|upright=1.4|La reconnaissance vocale est habituellement traitée dans le middleware ; les résultats sont transmis aux applications utilisatrices. La reconnaissance automatique de la pa

Arbre de décision

vignette| Arbre de décision Un arbre de décision est un outil d'aide à la décision représentant un ensemble de choix sous la forme graphique d'un arbre. Les différentes décisions possibles sont située

Arbre de décision (apprentissage)

L’apprentissage par arbre de décision désigne une méthode basée sur l'utilisation d'un arbre de décision comme modèle prédictif. On l'utilise notamment en fouille de données et en apprentissage automa