Time-domain isolated phoneme classification using reconstructed phase spaces

Michael T. Johnson, Richard J. Povinelli, Andrew C. Lindgren, Jinjin Ye, Xiaolin Liu, Kevin M. Indrebo

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy.

Original languageEnglish
Pages (from-to)458-465
Number of pages8
JournalIEEE Transactions on Speech and Audio Processing
Volume13
Issue number4
DOIs
StatePublished - Jul 2005

Keywords

  • Nonlinear systems
  • Phoneme classification
  • Reconstructed phase space
  • Speech recognition

ASJC Scopus subject areas

  • Software
  • Acoustics and Ultrasonics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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