Toward Robust Parametric Trajectory Segmental Model for Vowel Recognition

Abstract

In this paper we present a robust and discriminative segmental trajectory modeling for vowel recognition. We proposed two new approaches. One is using weighted least square estimation for the parametric trajectory parameter, which gives a much more robust performance over traditional least square estimation approach. The other is a specifically designed transformation matrix proposed to reduce the possible mismatch between the Gaussian modeling assumption and the trajectory feature's nature. Our experiments on the vowel classification using the mobile phone data of SpeechDAT(II) MDB showed significant improvement over both standard HMM and traditional segmental modeling.