Non Linear and Discriminant Feature Extraction Applied to Phonemes Recognition. Extraction de Caractéristiques Non Linéaire et Discriminante: Application À la Classification de Phonèmes

Non Linear and Discriminant Feature Extraction Applied to Phonemes Recognition

Extraction de Caractéristiques Non Linéaire et Discriminante: Application À la Classification de Phonèmes

Bruno Gas Mohamed Chetouani  Jean Luc Zarader 

Université Pierre et Marie Curie-Paris 6, Groupe Perception et Réseaux Connexionnistes, EA 2385, Ivry sur Seine, F-94200 France

Page: 
39-58
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Received: 
1 October 2005
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

In this article,we propose to study a speech coding method applied to the recognition of phonemes.The proposed model (the Neural Predictive Coding,NPC) and its two declinations (NPC-2 and DFE-NPC) is a connectionist model (multilayer perceptron) based on the non linear prediction of the speech signal.We show that it is possible to improve the discriminant capacities of such an encoder with the introduction of signal membership class information as from the coding stage.As such,it fits in with the category of DFE encoders (Discriminant Features Extraction) already proposed in literature.In this study we present a theoretical validation of the model in the hypothesis of unnoised signals and gaussian noised signals.NPC performances are compared to that obtained with traditional methods used to process speech on the Darpa Timit an Ntimit speech bases.Simulations presented here show that the classification rates are clearly improved compared to usual methods,in particular regarding phonemes considered difficult to process.A small vocabulary word recognition experiment is provided to show how NPC features can be used in a more conventional speech ANN-HMM based system approach.

Résumé

Nous proposons dans cet article une nouvelle méthode d'extraction de caractéristiques appliquée à la reconnaissance de phonèmes. Le modèle proposé:le codage neuronal prédictif (NPC pour Neural Predictive Coding) et ses deux déclinaisons NPC-2 et DFE-NPC (Discriminant Feature Extraction - NPC),est un modèle connexionniste de type perceptron multicouches (PMC) basé sur la prédiction non linéaire du signal de parole. Nous montrons qu'il est possible d'améliorer les capacités discriminantes d'un tel codeur en exploitant des informations de classe d'appartenance phonétique des signaux dès l'étape d'analyse. À ce titre,il entre dans la catégorie des extracteurs DFE déjà proposés dans la littérature.  Dans cette étude,nous présentons une validation théorique du modèle dans l'hypothèse de signaux respectivement non bruités et bruités (bruit additif gaussien). Les performances de l'extracteur NPC pour la classification de phonèmes sont comparées avec celles obtenues par les méthodes traditionnellement utilisées en extraction de caractéristiques sur des signaux des bases Darpa Timit et Ntimit. Les simulations présentées montrent que les taux de reconnaissance sont nettement améliorés,en particulier dans le cas de phonèmes de la langue anglaise fréquents mais réputés délicats à catégoriser. Enfin,une application en reconnaissance de mots isolés et petit vocabulaire est présentée dans le but de montrer comment l'on peut insérer les paramètres NPC dans une application de reconnaissance à l'aide d'un système mixte ANN-HMM (Artificial Neural Networks – Hidden Markov Models).

Keywords: 

Speech feature extraction,prédictive neural networks,nonlinear signal processing,phonemes recognition.

Mots clés

Extraction de caractéristiques,réseaux de neurones prédictifs,traitement non linéaire du signal,reconnaissance de phonèmes.

1. Introduction
2. L’état de l’art en Extraction de Caractéristiques pour la Classification
3. CodagePrédictif Neuronal: le Modèle NP
4. Extraction Discriminante de Caractéristiques
5. Analyse des Modèles
6. Conclusion
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