Open Access

A Support Vector Machine-Based Dynamic Network for Visual Speech Recognition Applications

  • Mihaela Gordan1Email author,
  • Constantine Kotropoulos1 and
  • Ioannis Pitas1
EURASIP Journal on Advances in Signal Processing20022002:427615

DOI: 10.1155/S1110865702207039

Received: 26 November 2001

Published: 28 November 2002

Abstract

Visual speech recognition is an emerging research field. In this paper, we examine the suitability of support vector machines for visual speech recognition. Each word is modeled as a temporal sequence of visemes corresponding to the different phones realized. One support vector machine is trained to recognize each viseme and its output is converted to a posterior probability through a sigmoidal mapping. To model the temporal character of speech, the support vector machines are integrated as nodes into a Viterbi lattice. We test the performance of the proposed approach on a small visual speech recognition task, namely the recognition of the first four digits in English. The word recognition rate obtained is at the level of the previous best reported rates.

Keywords

visual speech recognition mouth shape recognition visemes phonemes support vector machines Viterbi lattice

Authors’ Affiliations

(1)
Department of Informatics, Aristotle University of Thessaloniki

Copyright

© Gordan et al. 2002