An exemplar-based hidden markov model with discriminative visual features for lipreading

Xin Liu, Yiu Ming CHEUNG*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, we address an exemplar-based hidden markov model (HMM) that represents the lip motion activity using visual cues for lipreading. The discriminative visual features including the geometric shape parameters and contour-constrained spatial histogram are selected for representing each lip frame. Then, a set of exemplars associated with the HMM is learned jointly to serve as a typical representation of a speech utterance. Based on these exemplars, the high-dimensional frame features are transformed to the lower dimensional ones, namely Frame to Exemplar Distance (FED) vector. Subsequently, a continuous HMM is trained using such FED vector sequences for learning and recognition. Experiments show the promising results.

Original languageEnglish
Title of host publicationProceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages90-93
Number of pages4
ISBN (Electronic)9781479974344
DOIs
Publication statusPublished - 20 Jan 2015
Event10th International Conference on Computational Intelligence and Security, CIS 2014 - Kunming, Yunnan, China
Duration: 15 Nov 201416 Nov 2014

Publication series

NameProceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014

Conference

Conference10th International Conference on Computational Intelligence and Security, CIS 2014
Country/TerritoryChina
CityKunming, Yunnan
Period15/11/1416/11/14

Scopus Subject Areas

  • Computational Theory and Mathematics
  • Artificial Intelligence

User-Defined Keywords

  • Exemplar
  • FED vector
  • HMM
  • Lipreading

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