Recognition of sign language subwords based on boosted hidden Markov models

Proceedings of the Seventh International Conference on Multimodal Interfaces, ICMI'05

Volumn , Issue , 2005, Pages 282-287

  Zhang, Liang Guo ^a,c   Chen, Xilin ^a,b   Wang, Chunli ^a   Chen, Yiqiang ^a   Gao, Wen ^a,b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

AdaBoost; HMM; Human computer interaction; Sign language recognition

Indexed keywords

DECISION THEORY; FORMAL LANGUAGES; ITERATIVE METHODS; MARKOV PROCESSES; MATHEMATICAL MODELS; SPEECH COMMUNICATION;

ADAPTIVE BOOSTING (ADABOOST); CHINESE SIGN LANGUAGE (CSL); HIDDEN MARKOV MODELS (HMM); SIGN LANGUAGE RECOGNITION (SLR);

HUMAN COMPUTER INTERACTION;

EID: 32344451798     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1088463.1088511     Document Type: Conference Paper

References (21)

1. J.S. Kim, W. Jang, and Z. Bien. A dynamic gesture recognition system for the Korean sign language (KSL). IEEE Trans. Systems, Man, and Cybernetics, vol. 26, no. 2, 1996, 354-359.
2. R.H. Liang, and M. Ouhyoung. A real-time continuous gesture recognition system for sign language. In Proc. Third Int'l Conf. Automatic Face and Gesture Recognition (FGR), 1998, Nara, 558-565.
3. W. Gao, J.Y. Ma, J.Q. Wu, and C.L. Wang. Sign language recognition based on HMM/ANN/DP. Int'l J. Pattern Recognition and Artificial Intelligence, vol. 14, no. 5, 2000, 587-602.
4. T. Starner, J. Weaver, and A. Pentland. Real-time American sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 12, 1998, 1371-1375.
5. C. Vogler and D. Metaxas. Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In Proc. IEEE Int'l Conf. Systems, Man and Cybernetics (SMC), 1997, 156-161.
6. C. Vogler and D. Metaxas. A framework for recognizing the simultaneous aspects of American sign language. Computer Vision and Image Understanding 81, 2001, 358-384.
7. K. Grobel and M. Assan. Isolated sign language recognition using hidden Markov models. In Proc. IEEE Int'l Conf. SMC, 1996, 162-167.
8. B.Bauer and H. Hienz. Relevant features for video-based continuous sign language recognition, In Proc. FGR, 2000, 440-445.
9. L.-G. Zhang, Y. Chen, G. Fang, X. Chen, W. Gao. A vision-based sign language recognition system using tied-mixture density HMM. In ACM Sixth Int'l Conf. Multimodal Interfaces (ICMI), 2004, 198-204.
10. E.-J. Ong and R. Bowden. A boosted classifier tree for hand shape detection. In Proc. FGR, 2004.
11. H. Schwenk. Using boosting to improve a hybrid HMM / neural network speech recognizer. In Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing (ICASSP), 1999.
12. G. Zweig, M. Padmanadhan. Boosting Gaussian mixtures in an LVCSR system. In Proc. ICASSP, 2000.
13. C. Meyer. Utterance-level boosting of HMM speech recognizers. In Proc. ICASSP, 2002.
14. S.W. Foo and L. Dong. A boosted multi-HMM classifier for recognition of visual speech elements. In Proc. ICASSP, 2003.
15. P. Yin, I. Essa, J.M. Rehg. Asymmetrically boosted HMM for speech reading. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
16. C. Wang, W. Gao, and S. Shan. An approach based on phonemes to large vocabulary Chinese sign language recognition. In Proc. FGR, 2002, 411-416.
17. L.R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, vol. 77, no. 2, 1989, 257-285.
18. R.E. Schapire. The boosting approach to machine learning: an overview. In MSRI Workshop on Nonlinear Estimation and Classification, 2002.
19. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Science, vol. 55, no. 1, 1997, 119-139.
20. L.M. Arslan, J.H.L. Hansen. Selective training for hidden Markov models with application to speech recognition. IEEE Trans. Speech and Audio Processing, vol. 7, no. 1, 1999, 46-54.
21. P.J. Phillips, H. Moon, S.A. Rizvi, and P.J. Rauss. The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, 2000, 1090-1104.