Non-linear speech feature extraction for phoneme classification and speaker recognition

Authors:
Mohamed Chetouani;Marcos Faundez-Zanuy;Bruno Gas;Jean-Luc Zarader
Affiliations:
Laboratoire des Instruments et Systèmes d'Ile-De-France, Université Paris VI, Paris, France;Escola Universitària Politècnica de Mataró, Barcelona, Spain;Laboratoire des Instruments et Systèmes d'Ile-De-France, Université Paris VI, Paris, France;Laboratoire des Instruments et Systèmes d'Ile-De-France, Université Paris VI, Paris, France
Venue:
Nonlinear Speech Modeling and Applications
Year:
2005

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Abstract

In this paper we propose a new feature extraction algorithm based on non-linear prediction: the Neural Predictive Coding (NPC) model which is an extension of the classical LPC one. We apply this model to two significant tasks: phoneme classification and speaker identification. For the first one, the NPC model is trained with a Minimum Classification Error (MCE) criterion. The experiments carried out with the NTIMIT database show an improvement of the classification rates. For speaker identification, we propose a new feature extraction principle based on the NPC model. We also investigate different initialization methods. The new method gives better performances than the traditional ones (LPC, MFCC and PLP).