Phoneme Classification over the Reconstructed Phase Space using Principal Component Analysis

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

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
StatePublished - 2003
Event2003 ISCA Tutorial and Research Workshop on Nonlinear Speech Processing, NOLISP 2003 - Le Croisic, France
Duration: May 20 2003May 23 2003

Conference

Conference2003 ISCA Tutorial and Research Workshop on Nonlinear Speech Processing, NOLISP 2003
Country/TerritoryFrance
CityLe Croisic
Period5/20/035/23/03

Bibliographical note

Publisher Copyright:
© NOLISP 2003. All Rights Reserved.

ASJC Scopus subject areas

  • Communication
  • Artificial Intelligence
  • Signal Processing
  • Linguistics and Language

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