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Hidden conditional random fields for visual speech recognition

Hidden conditional random fields for visual speech recognition

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Info

Original languageEnglish
Title of host publicationProceedings 13th International Machine Vision and Image Processing Conference, 2009
Subtitle of host publicationIMVIP '09.
EditorsKen Dawson-Howe, Rozenn Dahyot, Anil Kokaram, Gerard Lacey
Place of PublicationLos Alamitos, Calif.
PublisherIEEE
Pages117-122
Number of pages6
ISBN (Electronic)9780769537962
ISBN (Print)9781424448753
DOIs
StatePublished - 2009
Event13th International Machine Vision and Image Processing Conference - Dublin, Ireland

Conference

Conference13th International Machine Vision and Image Processing Conference
CountryIreland
CityDublin
Period2/09/094/09/09

Abstract

In this paper we present the application of hidden conditional random fields (HCRFs) to modeling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.

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