Discriminative feature extraction based on PCA Gaussian mixture models

Authors:
Hossein Marvi
Affiliations:
Shahrood University of Technology, Faculty of Electrical Engineering and Robotics, Shahrood, Iran
Venue:
DNCOCO'06 Proceedings of the 5th WSEAS international conference on Data networks, communications and computers
Year:
2006

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Abstract

A discriminative feature extraction based on Principal component analysis (PCA) and Gaussian mixture models is presented to increase the discrimative capability of modified two dimentional root cepstrum analysis (MTDRC). The exprimental results show that F-ratio tests indicate better separability of phonemes by using discriminative feature extraction than MTDRC.