On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels [article]

Imen Trabelsi, Dorra Ben Ayed
2014 arXiv   pre-print
The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different features extraction methods such as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) with several features normalization techniques like rasta filtering and cepstral mean subtraction (CMS). Based on this, a comparative evaluation of these
more » ... ures is performed on the task of text independent speaker identification using a combination between gaussian mixture models (GMM) and linear and non-linear kernels based on support vector machine (SVM).
arXiv:1406.7314v1 fatcat:syiy6p3klfepvakmd2hp6zgd6q