Support vector machines for noise robust ASR

Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009

Volumn , Issue , 2009, Pages 205-210

  Gales, M J F ^a   Ragni, A ^a   AlDamarki, H ^a   Gautier, C ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; DISCRIMINATIVE CLASSIFIERS; DYNAMIC DATA; MULTI-CLASS CLASSIFICATION; MULTI-CLASS PROBLEMS; ROBUST ASR; TRAINING DATA; TREE-BASED;

CLASSIFIERS; HIDDEN MARKOV MODELS; SUPPORT VECTOR MACHINES;

SPEECH RECOGNITION;

EID: 77949375556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASRU.2009.5372913     Document Type: Conference Paper

References (21)

1. V. Venkataramani, S. Chakrabartty, and W. Byrne, "Support vector machines for segmental minimum Bayes risk decoding of continuous speech," in ASRU, 2003.
2. V. Vapnik, Statistical learning theory. John Wiley & Sons, 1998.
3. M. Gales and F. Flego, "Discriminative classiers with generative kernels for noise robust speech recognition," in Proc. ICASSP, 2009.
4. N. Smith and M. Gales, "Speech recognition using SVMs," in Adv. in Neural Inf. Proc. Systems, 2001.
5. M. Layton, "Augmented statistical models for classifying sequence data," Ph.D. dissertation, Cambridge University, 2006.
6. A. Acero, L. Deng, T. Kristjansson, and J. Zhang, "HMM Adaptation using Vector Taylor Series for Noisy Speech Recognition," in Proc. ICSLP, Beijing, China, 2000.
7. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2nd ed. Wiley-Interscience, 2001.
8. K. Weston and C. Watkins, "Support vector machines for multi-class pattern recognition," in Proc. Euro. Symp. Art. Neural Networks, 1999.
9. K. Crammer and Y. Singer, "On the algorithmic implementation of multi-class SVMs," Journal Machine Learning Research, 2001.
10. T. Jaakkola and D. Haussler, "Exploiting generative models in disciminative classifiers," in Adv. in Neural Inf. Proc. Systems 11, 1999.
11. A. Beygelzimer, K. Langford, and P. Ravikumar, "Multiclass classification with filter trees," in ITA Workshop, February 2007.
12. B. Kijsirikul and S. Abe, "Multiclass support vector machines using adaptive directed acyclic graphs," in Int. Joint Conf. Neural Netw., 2002.
13. J. Platt, N. Christianini, and J. Shawe-Taylor, "Large margin DAGs for multiclass classification," in Adv. in Neural Inf. Proc. Systems 12, 2000.
14. K. Tokuda, H. Zen, and A. Black, Text to speech synthesis: New paradigms and advances. Prentice Hall, 2004.
15. J. Li, L. Deng, Y. Gong, and A. Acero, "HMM adaptation with joint compensation of additive and convolutive distortions via vector Taylor series," in ASRU 2007, Kyoto, Japan, 2007.
16. H. Liao and M. Gales, "Joint uncertainty decoding for robust large vocabulary speech recognition," Cambridge University, Tech. Rep. CUED/F-INFENG/TR552, November 2006.
17. C. Gautier, "Filter trees for combining binary classifiers," M.Phil. dissertation, Cambridge University, 2008.
18. V. Vural and J. Dy, "A hierarchical method for multi-class support vector machines," in Proc. ICML, 2004.
19. K. Tokuda, H. Zen, and T. Kitamura, "Reformulating the HMM as a trajectory model," in Proc. Beyond HMM Workshop, December 2004.
20. H. AlDamarki, "Filter trees for noise robust small vocabulary speech recognition," M.Phil. dissertation, Cambridge University, 2009.
21. H.-G. Hirsch and D. Pearce, "The AURORA experimental framework for the evaluation of speech recognition systems under noisy conditions," in ASR-2000, 2000.