en fr Maximum-likelihood linear regression coefficients as features for speaker recognition Utilisation des coefficients de régression linéaire par maximum de vraisemblance comme paramètres pour la reconnaissance automatique dReport as inadecuate




en fr Maximum-likelihood linear regression coefficients as features for speaker recognition Utilisation des coefficients de régression linéaire par maximum de vraisemblance comme paramètres pour la reconnaissance automatique d - Download this document for free, or read online. Document in PDF available to download.

1 LIMSI - Laboratoire d-Informatique pour la Mécanique et les Sciences de l-Ingénieur

Abstract : The goal of this thesis is to find new and efficient features for speaker recognition. We are mostly concerned with the use of the Maximum-Likelihood Linear Regression MLLR family of adaptation techniques as features in speaker recognition systems. MLLR transformcoefficients are able to capture speaker cues after adaptation of a speaker-independent model using speech data. The resulting supervectors are high-dimensional and no underlying model guiding its generation is assumed a priori, becoming suitable for SVM for classification. This thesis brings some contributions to the speaker recognition field by proposing new approaches to feature extraction and studying existing ones via experimentation on large corpora: 1. We propose a compact yet efficient system, MLLR-SVM, which tackles the issues of transcript- and language-dependency of the standard MLLR-SVM approach by using single-class Constrained MLLR CMLLR adaptation transforms together with Speaker Adaptive Training SAT of a Universal Background Model UBM. 1- When less data samples than dimensions are available. 2- We propose several alternative representations of CMLLR transformcoefficients based on the singular value and symmetric-skew-symmetric decompositions of transform matrices. 3- We develop a novel framework for feature-level inter-session variability compensation based on compensation of CMLLR transform supervectors via Nuisance Attribute Projection NAP. 4- We perform a comprehensive experimental study of multi-class CMLLR-SVM systems alongmultiple axes including front-end, type of transform, type fmodel,model training and number of transforms. 5- We compare CMLLR and MLLR transform matrices based on an analysis of properties of their singular values. 6- We propose the use of lattice-basedMLLR as away to copewith erroneous transcripts in MLLR-SVMsystems using phonemic acoustic models.

en fr

Keywords : speech processing speaker recognition speaker adaptation support vector machine

Mots-clés : traitement du langage MLLR reconnaissance du locuteur adaptation du locuteur





Author: Marc Ferràs Font -

Source: https://hal.archives-ouvertes.fr/



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