A hybrid approach for automatic lip localization and viseme classification to enhance visual speech recognition

  • Authors:
  • Walid Mahdi;Salah Werda;Abdelmajid Ben Hamadou

  • Affiliations:
  • Multimedia Information systems and Advanced Computing Laboratory, High Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia. E-mail: walid.mahdi@isimsf.rnu.tn;Multimedia Information systems and Advanced Computing Laboratory, High Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia. E-mail: walid.mahdi@isimsf.rnu.tn;Multimedia Information systems and Advanced Computing Laboratory, High Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia. E-mail: walid.mahdi@isimsf.rnu.tn

  • Venue:
  • Integrated Computer-Aided Engineering
  • Year:
  • 2008

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Abstract

An automatic lip-reading system is among assistive technologies for hearing impaired or elderly people. We can imagine, for example, a dependent person ordering a machine with an easy lip movement or by a simple visemes (visual phoneme) pronunciation. A lip-reading system is decomposed into three subsystems: a lip localization subsystem, then a feature extracting subsystem, followed by a classification system that maps feature vectors to visemes. The major difficulty in a lip-reading system is the extraction of the visual speech descriptors. In fact, to ensure this task it is necessary to carry out an automatic localization and tracking of the labial gestures. We present, in this paper, a new automatic approach for lip POI localization and feature extraction on a speaker's face based on mouth color information and a geometrical model of the lips. The extracted visual information is then classified in order to recognize the uttered viseme. We have developed our Automatic Lip Feature Extraction prototype (ALiFE). ALiFE prototype is evaluated for multiple speakers under natural conditions. Experiments include a group of French visemes for different speakers. Results revealed that our system recognizes 94.64% of the tested French visemes.