Start-synchronous search for large vocabulary continuous speech recognition

IEEE Transactions on Speech and Audio Processing

Volumn 7, Issue 5, 1999, Pages 542-553

  Renais, Steve ^a,b   Hochberg, Michael M ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b NUANCE COMMUNICATIONS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; SPEECH ANALYSIS; SPEECH SYNTHESIS; SYNCHRONIZATION;

LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION; PHONE DEACTIVATION PRUNING;

CONTINUOUS SPEECH RECOGNITION;

EID: 0032595186     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/89.784107     Document Type: Article

References (37)

1. L. R. Bahl, F. Jelinek, and R. L. Mercer, "A maximum likelihood approach to continuous speech recognition," IEEE Trans. Pattern Anal. Machine Intell., vol. PAM1-5, pp. 179-190, 1983.
2. A. B. Poritz, "Hidden Markov models: A guided tour," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, New York, 1988, pp. 7-13.
3. G. D. Fomey, Jr., "The Viterbi algorithm," Proc. IEEE, vol. 61, pp. 268-278, 1973.
4. T. K. Vmtsyuk, "Element-wise recognition of continuous speech composed of words from a specified dictionary," Kibernetika, vol. 7, pp. 133-143, 1971.
5. S. Ortmanns, H. Ney, and X. Aubert, "A word graph algorithm for large vocabulary continuous speech recognition," Compitt. Speech Lang., vol. 11, pp. 43-72, 1997.
6. B. Lowerre and R. Reddy, "The Harpy speech understanding system," in Trends in Speech Recognition, W. A. Lea, Ed. Englewood Cliffs, NJ: Prentice-Hall, 1980.
7. H. Ney, D. Mergel, A. Noll, and A. Paesler, "Data-driven search organization for continuous speech recognition," IEEE Trans. Signal Processing, vol. 40, pp. 272-281, 1992.
8. J. J. Odell, "The use of context in large vocabulary speech recognition," Ph.D. dissertation, Univ. Cambridge, Cambridge, U.K., 1995.
9. R. Schwartz, L. Nguyen, and J. Makhoul, "Multiple-pass search strategies," in Automatic Speech and Speaker Recognition Advanced Topics, C.-H. Lee, F. K. Soong, and K. K. Paliwal, Eds. Boston, MA: Kluwer 1996, ch. 18, pp. 429-456.
10. F. Jelinek, "Fast sequential decoding algorithm using a stack," IBM J. Res. Develop., vol. 13, pp. 675-685, 1969.
11. N. J. Nilsson, Problem Solving Methods of Artificial Intelligence. New York: McGraw-Hill, 1971.
12. F. Jelinek, L. R. Bahl, and R. L. Mercer, "Design of a linguistic statistical decoder for the recognition of continuous speech," IEEE Trans. Inform. Theory, vol. IT-21, pp. 250-256, 1975.
13. L. R. Bahl and F. Jelinek, "Apparatus and method for determining a likely word sequence from labels generated by an acoustic processor," U.S. Patent 4748670, May 1988.
14. P. S. Gopalakrishnan, L. R. Bahl, and R. L. Mercer, "A tree-search strategy for large vocabulary continuous speech recognition," in Proc. Int. ConJ. Acoustics, Speech and Signal Processing, Detroit, MI, 1995, vol. 1, pp. 572-575.
15. D. B. Paul, "An efficient A* stack decoder algorithm for continuous speech recognition with a stochastic language model," in Proc. Int. Con}. Acoustics, Speech and Signal Processing, San Francisco, CA, 1992, vol. 1, pp. 25-28.
16. P. Kenny et al., "A*-admissibIe heuristics for rapid lexical access," IEEE Trans. Speech Audio Processing, vol. 1, pp. 59-58, 1993.
17. F. K. Soong and E.-F. Huang, "A tree-trellis based fast search for finding the n-best sentence hypotheses in continuous speech recognition," in Proc. Int. Co/iJ. Acoustics, Speech and Signal Processing, Toronto, Ont., Canada, 1991, vol. 1, pp. 705-708.
18. L. R. Bahl and F. Jelinek, "Decoding for channels with insertions, deletions and substitutions with applications to speech recognition," IEEE Trans. Inform. Theory; vol. 21, pp. 404-411, 1975.
19. M. M. Hochberg, S. J. Renais, A. J. Robinson, and G. D. Cook, "Recent improvements to the Abbot large vocabulary CSR system," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Detroit, MI, 1995, pp. 69-72.
20. T. Robinson, M. Hochberg, and S. Renais, "The use of recurrent networks in continuous speech recognition," in Automatic Speech and Speaker Recognition Advanced Topics, C. H. Lee, K. K. Paliwal, and F. K. Soong, Eds. Boston, MA: Kluwer, 1996, ch. 10, pp. 233-258.
21. H. Bourlard and N. Morgan, Connectionist Speech Recognition-A Hybrid Approach. Boston; MA: Kluwer, 1994.
22. A. J. Robinson, "The application of recurrent nets to phone probability estimation," IEEE Trans. Neural Nenvorks, vol. 5, pp. 298-305, 1994.
23. J. Hennebert, C. Ris, H. Bourlard, S. Renais, and N. Morgan, "Estimation of global posteriors and forward-backward training of hybrid HMM/ANN systems," in Proc. Europ. Conf. Speech Communication and Technology, Rhodes, Greece, 1997, pp. 1951-1954.
24. T. H. Cormen, C. E. Leiserson, and R. L. Rivest, Introduction to Algorithms. Cambridge, MA: MIT Press, 1990.
25. S. Renais et al., "Connectionist probability estimators in HMM speech recognition," IEEE Trans. Speech Audio Processing, vol. 2, pp. 161-175, 1994.
26. D. J. Kershaw, M. M. Hochberg, and A. J. Robinson, "Contextdependent classes in a hybrid recurrent network-HMM speech recognition system," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1996, vol. 8.
27. V. Gupta, M. Lennig, and P. Mermelstein, "Fast search strategy in a large vocabulary speech recognizer," J. Acoust. Soc. Amer., vol. 84, pp. 2007-2017, 1988.
28. L. R. Bahl, S. V. De Gennaro, P. S. Gopalakrishnan, and R. L. Mercer, "A fast approximate acoustic match for large vocabulary speech recognition," IEEE Trans. Speech Audio Processing, vol. 1, pp. 59-67, 1993.
29. R. Haeb-Umbach and H. Ney, "Improvements in beam search for 10 000-word continuous-speech recognition," IEEE Trans. Speech Audio Processing, vol. 2, pp. 353-356, 1994.
30. M. K. Ravishankar, Efficient Algorithms for Speech Recognition, Ph.D. dissertation, School Comput. Sei., Carnegie Mellon Univ., Pittsburgh, PA, 1996.
31. H. Bourlard, B. D'hoore, and J.-M. Boite, "Optimizing recognition and rejection performance in wordspotting systems," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Adelaide, Australia, 1994, vol. 1, pp. 373-376.
32. X. Aubert, C. Dugast, H. Ney, and V. Steinbiss, "Large vocabulary continuous speech recognition of Wall Street Journal data," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Adelaide, Australia, 1994, vol. 2, pp. 129-132.
33. S. Renais, "Phone deactivation pruning in large vocabulary continuous speech recognition," IEEE Signal Processing Lett., vol. 3, pp. 4-6, Jan. 1996.
34. P. S. Gopalakrishnan, D. Nahamoo, M. Padmanabhan, and M. A. Picheny, "A channel-bank-based phone detection strategy," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Adelaide, Australia, 1994, vol. 2, pp. 161-164.
35. T. Robinson et al., "WSJCAMO: A British English speech corpus for large vocabulary continuous speech recognition," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Detroit, MI, 1995, pp. 81-84.
36. R. Schwanz and S. Austin, "A comparison of several approximate algorithms for finding multiple (n-best) sentence hypotheses," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Toronto, Ont., Canada, 1991, pp. 701-704.
37. D. Willett, C. Neukirchen, and G. Rigoll, "Efficient search with posterior probability estimates in HMM-based speech recognition," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, Seattle, WA, 1998, pp. 821-824.