Close-class-set discrimination method for recognition of stop consonant-vowel utterances using support vector machines

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2130, Issue , 2001, Pages 399-404

  Sekhar, Chellu Chandra ^a   Takeda, Kazuya ^a   Itakura, Fumitada ^a  

^a NAGOYA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

LINGUISTICS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

CONVENTIONAL APPROACH; MULTICLASS PATTERN RECOGNITION; PATTERN RECOGNITION PROBLEMS; SUPPORT VECTOR MACHINE (SVMS);

PATTERN RECOGNITION;

EID: 84959010775     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_56     Document Type: Conference Paper

References (10)

1. C.J.C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.
2. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, 2000.
3. J. Weston and C. Watkins, “Multi-class support vector machines,” Tech. Rep. CSDTR- 98-04, Royal Holloway, University of London, May 1998.
4. P. Clarkson and P.J. Moreno, “On the use of support vector machines for phonetic classification,” in ICASSP, Mar. 1999, pp. 585–588.
5. P. Niyogi, C. Burges, and P. Ramesh, “Distinctive feature detection using support vector machines,” in ICASSP, Mar. 1999, pp. 425–428.
6. C. Chandra Sekhar, Neural Network Models for Recognition of Stop Consonant- Vowel (SCV) Segments in Continuous Speech, Ph.D. thesis, Indian Institute of Technology, Madras, 1996.
7. J.Y. Siva Rama Krishna Rao, C. Chandra Sekhar, and B. Yegnanarayana, “Neural networks based approach for detection of vowel onset points,” in International Conference on Advances in Pattern Recognition and Digital Techniques, Calcutta, Dec. 1999, pp. 316–320.
8. L. R. Rabiner and B. H. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, 1993.
9. R. Collobert and S. Bengio, “Support vector machines for large-scale regression problems,” Tech. Rep. IDIAP-RR-00-17, IDIAP, Switzerland, 2000.
10. T.S. Jaakkola and D. Haussler, “Exploiting generative models in discriminative classifiers,” in Advances in Neural Information Processing Systems, M.S.Kearns, S.A.Solla, and D.A.Cohn, Eds., vol. 11. MIT Press, 1998.