Hough transform-based mouth localization for audio-visual speech recognition

British Machine Vision Conference, BMVC 2009 - Proceedings

Volumn , Issue , 2009, Pages

  Fanelli, Gabriele ^a   Gall, Juergen ^a   Van Gool, Luc ^b  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

FACE RECOGNITION; HOUGH TRANSFORMS; SPEECH RECOGNITION;

AUDIO AND VISUAL CUES; AUDIO VISUAL SPEECH RECOGNITION; DIFFERENT SHAPES; FACIAL FEATURE POINTS; MULTI-MODAL SPEECH RECOGNITION; RECOGNITION ACCURACY; ROTATION INVARIANT; TRANSFORM-BASED METHODS;

COMPUTER VISION;

EID: 84898888944     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.23.78     Document Type: Conference Paper

References (29)

1. Y. Amit and D. Geman. Shape quantization and recognition with randomized trees. Neural Computation, 9(7):1545-1588, 1997.
2. D. H. Ballard. Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition, 13(2):111-122, 1981.
3. C. Bregler and S. Omohundro. Nonlinear manifold learning for visual speech recognition. In International Conference on Computer Vision, pages 494-499, 1995.
4. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
5. D. Cristinacce, T. Cootes, and I. Scott. A multi-stage approach to facial feature detection. In British Machine Vision Conference, London, England, pages 277-286, 2004.
6. J. Gall and V. Lempitsky. Class-specific hough forests for object detection. In IEEE Conference on Computer Vision and Pattern Recognition, 2009.
7. M. Gordan, C. Kotropoulos, and I. Pitas. A support vector machine-based dynamic network for visual speech recognition applications. EURASIP J. Appl. Signal Process., 2002(1):1248-1259, 2002.
8. H. Grabner, M. Grabner, and H. Bischof. Real-time tracking via on-line boosting. In British Machine Vision Conference, Volume 1, pages 47-56, 2006.
9. M. S. Gray, J. R. Movellan, and T. J. Sejnowski. Dynamic features for visual speechreading: A systematic comparison. In Neural Information Processing System Conference (NIPS), pages 751-757, 1996.
10. M. Gurban and J. P. Thiran. Information theoretic feature extraction for audio-visual speech recognition. IEEE Transactions on Signal Processing, 2009.
11. M. Heckmann, F. Berthommier, and K. Kroschel. A hybrid ann/hmm audio-visual speech recognition system. In International Conference on Auditory-Visual Speech Processing, 2001.
12. R. Kaucic, B. Dalton, and A. Blake. Real-time lip tracking for audio-visual speech recognition applications. In European Conference on Computer Vision, pages 376-387, 1996.
13. B. Leibe, A. Leonardis, and B. Schiele. Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision, 77(1-3):259-289, 2008.
14. J. Luettin and N. A. Thacker. Speechreading using probabilistic models. Computer Vision and Image Understanding, 65(2):163-178, 1997.
15. I Matthews, J. A. Bangham, R. Harvey, and S. Cox. A comparison of active shape model and scale decomposition based features for visual speech recognition. In European Conference on Computer Vision, pages 514-528, 1998.
16. H. McGurk and J. MacDonald. Hearing lips and seeing voices. Nature, 264:746-748, 1976.
17. S. Pachoud, S. Gong, and A. Cavallaro. Macro-cuboids based probabilistic matching for lip-reading digits. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.
18. E. K. Patterson, S. Gurbuz, Z. Tufekci, and J.N. Gowdy. Moving-talker, speaker-independent feature study, and baseline results using the cuave multimodal speech corpus. EURASIP J. Appl. Signal Process., 2002(1):1189-1201, 2002. ISSN 1110-8657.
19. E.D. Petajan. Automatic lipreading to enhance speech recognition. In IEEE Communication Society Global Telecommunications Conference, 1984.
20. G. Potamianos and P. Scanlon. Exploiting lower face symmetry in appearance-based automatic speechreading. In Audio-Visual Speech Process., pages 79-84, 2005.
21. G. Potamianos, H. P. Graf, and E. Cosatto. An image transform approach for hmm based automatic lipreading. In International Conference on Image Processing, pages 173-177, 1998.
22. G. Potamianos, C. Neti, J. Luettin, and I. Matthews. Issues in Visual and Audio Visual Speech Processing, chapter Audio-Visual Automatic Speech Recognition: An Overview. MIT Press, 2004.
23. BioID Technology Research, 2001. http://www.bioid.de/.
24. Q. Summerfield. Lipreading and audio-visual speech perception. In Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, Volume 335, pages 71-78, 1992.
25. R. Valenti and T. Gevers. Accurate eye center location and tracking using isophote curvature. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.
26. P. Viola and M. Jones. Robust real-time object detection. In International Journal of Computer Vision, 2001.
27. D. Vukadinovic and M. Pantic. Fully automatic facial feature point detection using gabor feature based boosted classifiers. In IEEE International Conference on Systems, Man and Cybernetics, pages 1692-1698, 2005.
28. P. Yin, I. Essa, and J. M. Rehg. Asymmetrically boosted hmm for speech reading. In IEEE Conference on Computer Vision and Pattern Recognition, pages 755-761, 2004.
29. S. Young, D. Kershaw, J. Odell, D. Ollason, V. Valtchev, and P. Woodland. The HTK Book. Entropic Ltd., Cambridge, 1999.