Structural Maximum a Posteriori Adaptation for Mixture Stochastic Trajectory Framework

Irina Illina 1 Djamel Mostefa 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper we address the problem of the adaptation of a speech recognition system to a new environment. The aim of adaptation is to compensate the mismatch between training and testing conditions without retraining completely the recognition system. The questions are what has to be compensated and how? We propose to compensate the means and variances of the Gaussian pdfs, representing the acoustic models, using the linear transformations and ML and MAP estimations. To better take into account the variability of the adaptation data, the pdfs of models are organised in a tree. This tree structure is used also for the definition of prior densities of transformations. The approach is called Structural Maximum a Posteriori adaptation (SMAP). SMAP is developed for a segment-based model, the Mixture Stochastic Trajectory Model (MSTM). Experimental results on RM task for supervised speaker adaptation show that SMAP significantly outperforms the MLLR adaptation for the same amount of adaptation data and the same number of transformation parameters.
Type de document :
Communication dans un congrès
WorkShop International on Adaptation Methods for Automatic Speech Recognition, 2001, Sophia Antipolis, France, 1/1 (1), 4 p, 2001
Liste complète des métadonnées

https://hal.inria.fr/inria-00101103
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:56:31
Dernière modification le : jeudi 11 janvier 2018 - 06:19:55

Identifiants

  • HAL Id : inria-00101103, version 1

Collections

Citation

Irina Illina, Djamel Mostefa. Structural Maximum a Posteriori Adaptation for Mixture Stochastic Trajectory Framework. WorkShop International on Adaptation Methods for Automatic Speech Recognition, 2001, Sophia Antipolis, France, 1/1 (1), 4 p, 2001. 〈inria-00101103〉

Partager

Métriques

Consultations de la notice

154