Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/10872
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Title: Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation
Authors: Abel, Andrew
Hussain, Amir
Nguyen, Quoc-Dinh
Ringeval, Fabien
Chetouani, Mohamed
Milgram, Maurice
Contact Email: aka@cs.stir.ac.uk
Editors: Fierrez, J
Ortega-Garcia, J
Esposito, A
Drygajlo, A
Faundez-Zanuy, M
Citation: Abel A, Hussain A, Nguyen Q, Ringeval F, Chetouani M & Milgram M (2009) Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation. In: Fierrez J, Ortega-Garcia J, Esposito A, Drygajlo A, Faundez-Zanuy M (ed.). Biometric ID Management and Multimodal Communication: Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid, Spain: September 2009, Proceedings. Lecture Notes in Computer Science, 5707, Berlin, Germany: Springer-Verlag, pp. 65-72.
Issue Date: 2009
Publisher: Springer-Verlag
Series/Report no.: Lecture Notes in Computer Science, 5707
Abstract: In recent years, the established link between the various human communication production domains has become more widely utilised in the field of speech processing. In this work, a state of the art Semi Adaptive Appearance Model (SAAM) approach developed by the authors is used for automatic lip tracking, and an adapted version of our vowel based speech segmentation system is employed to automatically segment speech. Canonical Correlation Analysis (CCA) on segmented and non segmented data in a range of noisy speech environments finds that segmented speech has a significantly better audiovisual correlation, demonstrating the feasibility of our techniques for further development as part of a proposed audiovisual speech enhancement system.
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Type: Part of book or chapter of book
URI: http://hdl.handle.net/1893/10872
URL: http://www.springer.com/computer/image+processing/book/978-3-642-04390-1
Affiliation: Computing Science - CSM Dept
Computing Science - CSM Dept
University Pierre and Marie Curie (UPMC)
University Pierre and Marie Curie (UPMC)
University Pierre and Marie Curie (UPMC)
University Pierre and Marie Curie (UPMC)

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