Non-Linear Speech Processing (NOLISP 03)

May 20-23, 2003
Le Croisic, France

Phoneme Classification over the Reconstructed Phase Space using Principal Component Analysis

Jinjin Ye, Michael T. Johnson, Richard J. Povinelli

Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, USA

Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of the feature space is orthogonalized. A Bayes classifier uses the transformed feature vectors to classify phoneme exemplars. The results show that the classification accuracy with the PCA method surpasses the accuracy using only original features in most cases. PCA projection was implemented in three ways over the reconstructed phase space on both speaker-dependent and speaker-independent data. Models are trained and tested using data drawn from the TIMIT database.

Full Paper

Bibliographic reference.  Ye, Jinjin / Johnson, Michael T. / Povinelli, Richard J. (2003): "Phoneme classification over the reconstructed phase space using principal component analysis", In NOLISP-2003, paper 019.