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

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). © Springer-Verlag Berlin Heidelberg 2005.