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).
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Chetouani, M., Faundez-Zanuy, M., Gas, B., Zarader, JL. (2005). Non-linear Speech Feature Extraction for Phoneme Classification and Speaker Recognition. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_16
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DOI: https://doi.org/10.1007/11520153_16
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Print ISBN: 978-3-540-27441-4
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