Polynomial dynamic time warping kernel support vector machines for dysarthric speech recognition with sparse training data

9th European Conference on Speech Communication and Technology

Volumn , Issue , 2005, Pages 3321-3324

  Wan, Vincent ^a   Carmichael, James ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; ERROR ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; NEUROLOGY; POLYNOMIALS;

DYNAMIC TIME WARPING (DTW); ERROR RATE; HIDDEN MARKOV MODEL (HMM); SUPPORT VECTOR MACHINE (SVM);

SPEECH RECOGNITION;

EID: 33745208212     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. P. Green, J. Carmichael, A. Hatzis, P. Enderby, M. Hawley, and M. Parker, "Automatic speech recognition with sparse training data for dysarthric speakers," in Proc. Eurospeech, 2003.
2. J. Deller, D. Hsu, and L. Ferrier, "On the use of hidden Markov modelling for recognition of dysarthric speech," in Computer Methods and Programmes in Biomedicine, 1991, vol. 35, pp. 125-139.
3. B. Schölkopf, C. Burges, and A. Smola, Eds., Advances in Kernel Methods - Support Vector Learning, MIT Press, 1999.
4. L. Rabiner and B. H. Juang, Fundamentals of Speech Recognition, Prentice Hall, 1993.
5. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 1-47, 1998.
6. T. S. Jaakkola and D. Haussler, "Exploiting generative models in discriminative classifiers," in Advances in Neural Information Processing Systems 11, M. S. Kearns, S. A. Solla, and D. A. Cohn, Eds. MIT Press, 1998.
7. C. Watkins, "Dynamic alignment kernels," Tech. Rep., Royal Holloway College, University of London, 1999.
8. W. M. Campbell, "Generalised linear disciminant sequence kernels for speaker recognition," in Proc. ICASSP, 2002.
9. H. Shimodaira, K. Noma, M. Nakai, and S. Sagayama, "Support vector machine with dynamic time-alignment kernel for speech recognition," in Proc. Eurospeech, 2001.
10. C. Bahlmann, B. Haasdonk, and H. Burkhardt, "On-line handwriting recognition with support vector machines - a kernel approach," in Proc. 8th International Workshop on Frontiers in Handwriting Recognition, 2002, pp. 49-54.
11. V. Wan, Speaker Verification using Support Vector Machines, Ph.D. thesis, University of Sheffield, June 2003.
12. V. Wan and S. Renals, "Speaker verification using sequence discriminant support vector machines," IEEE Transactions on Speech and Audio Processing, vol. 13, no. 2, pp. 203-210, March 2005.
13. R. Courant and D. Hilbert, Methods of Mathematical Physics, Interscience, 1953.
14. S. J. Young, "The HTK HMM toolkit: Design and philosopy.," Tech. Rep., Univ. Cambridge, Dept. Eng., 1993.
15. A. Hatzis, P. Green, J. Carmichael, S. Cunningham, R. Palmer, M. Parker, and P. O'Neil, "An integrated toolkit deploying speech technology for computer based speech training to dysarthric speakers," in Proc. Eurospeech, 2003, pp. 2213-2216.
16. J. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin DAGs for multiclass classification," in Advances in Neural Information Processing Systems 12, 2000.