Lattice segmentation and support vector machines for large vocabulary continuous speech recognition

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn I, Issue , 2005, Pages

  Venkataraman, Veera ^a   Byrne, William ^a,b  

^a Johns Hopkins University   (United States)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; ERROR ANALYSIS; SPEECH ANALYSIS; STATISTICAL METHODS;

CONVERSATIONAL SPEECH; LATTICE SEGMENTATION; SUPPORT VECTOR MACHINES; VOCABULARY CONTINUOUS SPEECH RECOGNITION;

SPEECH RECOGNITION;

EID: 33645759970     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2005.1415239     Document Type: Conference Paper

References (16)

1. V. Doumpiotis, S. Tsakalidis, and W. Byrne, "Lattice segmentation and minimum Bayes risk discriminative training," in Eurospeech, 2003.
2. V. Doumpiotis and W. Byrne, "Lattice segmentation and minimum Bayes risk discriminative training for large vocabulary continuous speech recognition," Speech Communication, Submitted.
3. P. C. Woodland and D. Povey, "Minimum phone error and I-smoothing for improved discriminative training," in Proc. ICASSP, 2002.
4. V. Doumpiotis, S. Tsakalidis, and W. Byrne, "Discriminative training for segmental minimum Bayes risk decoding," in ICASSP, 2003.
5. V. Vapnik, The Nature of Statistical Learning Theory, chapter 5, Springer-Verlag, New York, Inc., 1995.
6. A. Ganapathiraju, J. Hamaker, and J. Picone, "Advances in hybrid SVM/HMM speech recognition," in GSPx / International Signal Processing Conference, 2003.
7. S. E. Golowich and D. X. Sun, "A support vector/hidden Markov model approach to phoneme recognition," in ASA Proceedings of the Statistical Computing Section, 1998, pp. 125-130.
8. J. Salomon, S. King, and M. Osburne, "Framewise phone classification using support vector machines," in Proc. ICSLP, 2002.
9. V. Venkataramani and W. Byrne, "Support vector machines for segmental minimum Bayes risk decoding of continuous speech," in ASRU, 2003.
10. V. Venkataramani, S. Chakarabarty, and W. Byrne, "Gzmsupport vector machines for segmental minimum Bayes risk decoding of continuous speech," Comp. Spch. Lang., Submitted.
11. V. Goel, S. Kumar, and W. Byrne, "Segmental minimum Bayes-risk decoding for automatic speech recognition," IEEE Transactions on Speech and Audio Processing, May 2004.
12. W. Byrne et al., "Automatic recognition of spontaneous speech for access to multilingual oral history archives," IEEE Trans. Speech and Audio Proc., July, 2004.
13. P. C. Woodland and D. Povey, "Large scale discriminative training for speech recognition," in Proc. ITW ASR, 2000.
14. N. D. Smith and M. J. F. Gales, "Using SVMs and discriminative models for speech recognition," in ICASSP, 2002.
15. S.Chakrabartty, The giniSVMtoolkit, Version 1.2, Available: http://bach.ece.jhu.edu/svm/ginisvm/, 2003.
16. D. Pallett, W. Fisher, and J. Fiscus., "Tools for the analysis of benchmark speech recognition tests.," in ICASSP, 1990.