RBF neural network mouth tracking for audio-visual speech recognition system

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Volumn A, Issue , 2004, Pages

  Lim, Ee Hui ^a   Seng, K P ^a   Tse, K M ^b  

^a MONASH UNIVERSITY MALAYSIA   (Malaysia)

^b GRIFFITH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; KALMAN FILTERING; MULTIMEDIA SYSTEMS; NEURAL NETWORKS; PERTURBATION TECHNIQUES; RADIAL BASIS FUNCTION NETWORKS;

ACOUSTIC NOISE PERTURBATION; AUDIO-VISUAL SPEECH RECOGNITION (AVSR); EXTENDED KALMAN FILTERS (EKF);

SPEECH RECOGNITION;

EID: 27944463669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. S. H. McGurk and J. MacDonald. Hearing lips and seeing voices. Nature, pages 746 748, September 1976.
2. R. Lienhart, J. Maydt, An extended set of Haar-like features for rapid objection detection, IEEE ICIP, pp.900-903, 2002
3. L. Rabiner and B.H. Huang. Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs, NJ, 1993.
4. A. V. Nefian, L. Liang, X. Pi, X. Liu, and C. Mao. An coupled hidden Markov model for audio-visual speech recognition. In international Conference on Acoustics, Speech and Signal Processing, 2002
5. M. D. Cordea, E. M. Petriu, N. D. Georganas, et al, Real-time 2(1/2)-D head pose recovery for model-based video-coding, IEEE Trans. on Instrumentation and Measurement, 50(4): pp. 1007-1013, 2001
6. H.Z Ai, L. Liang, and G.Y.Xu. Face detection in template matching constrained subspace. In IEEE International Conference on Artificial Intelligence, volume 2, pages 603 608, 2001.
7. H.A. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23 38, January 1998.
8. V.N. Vapnik. The Nature of Statistical Learning Theory. Berlin:Springer-Verlag, 1999.
9. J. Park and I. W Sandberg, "Universal approximation using RBF networks", Neural computation, vol.3, pp. 246-257, 1991.
10. M. T. Musavial et al., "On the training of radial basis function classifiers," Neural Networks, vol. 5, pp. 595 603, 1992.
11. J. Sum, C. Leung, G. Young, W. Kan, On the Kalman □tering method in neural network training and pruning, IEEE Trans. Neural Networks 10 (1999) 161-166.
12. M. Birgmeier, A fully Kalman-trained radial basis function network for nonlinear speech modeling, IEEE International Conference on Neural Networks, Perth, Western Australia, 1995, pp. 259 264.
13. Sung, K.-K.; Poggio, T. Example-based learning for view-based human face detection, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 20 Issue: 1, Jan. 1998 Page(s): 39-51