Modélisation de trajectoires et de classes de locuteurs pour la reconnaissance de voix d'enfants et d'adultes

International audienceWhen the speech data is produced by speakers of different age and gender, the acoustic variability of any given phonetic unit becomes large, which degrades speech recognition performance. One way to go beyond conventional Hidden Markov Model is to explicitly include speaker class information in the modeling. Speaker classes can be obtained automatically, and they are used for building speaker class-specific acoustic models. This paper introduces a structuring of the Gaussian components of the GMM densities with respect to the speaker classes. In a first approach, this structuring of the Gaussian components is completed with speaker class-dependent mixture weights, and in a second approach, with transition matrices, which add dependencies between Gaussian components of mixture densities (as in stranded GMMs). The two approaches bring substantial performance improvements when recognizing adult and child speech. Using class-structured components plus mixture transition matrices reduces by more than one third the word error rate on the TIDIGIT corpus.RÉSUMÉ Lorsque l'on considère de la parole produite par des enfants et des adultes, la variabilité acous-tique de chaque unité phonétique devient grande, ce qui dégrade les performances de recon-naissance. Un moyen d'aller au-delà des modèles de Markov traditionnels, est de prendre en considération des classes de locuteurs. Les classes de locuteurs peuvent être obtenues automa-tiquement. Elles servent à fabriquer des modèles acoustiques spécifiques de chaque classe. Ce papier propose une structuration des composantes des densités multigaussiennes (GMMs) en re-lation avec des classes de locuteurs. Dans une première approche, cette structuration des densités est complétée par des pondérations des composantes gaussiennes dépendantes des classes de locuteurs, et dans une deuxième approche, par des matrices de transition entre les composantes gaussiennes des densités (comme dans les stranded GMMs). Ces deux approches apportent des gains substantiels pour la reconnaissance de voix d'enfants et d'adultes. La structuration des composantes gaussiennes complétée par des matrices de transition entre composantes réduit de plus d'un tiers le taux d'erreur mot sur le corpus TIDIGIT

Structured GMM Based on Unsupervised Clustering for Recognizing Adult and Child Speech

International audienceSpeaker variability is a well-known problem of state-of-the art Automatic Speech Recognition (ASR) systems. In particular, handling children speech is challenging because of substantial differences in pronunciation of the speech units between adult and child speakers. To build accurate ASR systems for all types of speakers Hidden Markov Models with Gaussian Mixture Densities were intensively used in combinationwith model adaptation techniques.This paper compares different ways to improve the recognition of children speech and describes a novel approach relying on Class-StructuredGaussian Mixture Model (GMM). A common solution for reducing the speaker variability relies on gender and age adaptation. First, it is proposed to replace gender and age byunsupervised clustering. Speaker classes are first used for adaptation of the conventional HMM. Second, speaker classes are used for initializing structured GMM, where the components of Gaussian densities are structured with respect to the speaker classes. In a first approach mixture weights of the structured GMM are set dependent on the speaker class. In a second approach the mixture weights are replaced by explicit dependencies between Gaussian components of mixture densities (as in stranded GMMs, but here the GMMs are class-structured).The different approaches are evaluated and compared on the TIDIGITS task. The best improvement is achieved when structured GMM is combined with feature adaptation