|Appears in Collections:||Computing Science and Mathematics Book Chapters and Sections|
|Title:||Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation|
|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 (eds.) 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. http://www.springer.com/computer/image+processing/book/978-3-642-04390-1; https://doi.org/10.1007/978-3-642-04391-8_9|
|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.|
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