Toward robust parametric trajectory segmental model for vowel recognition

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.