Modeling partial pronunciation variations for spontaneous Mandarin speech recognition

Computer Speech and Language

Volumn 17, Issue 4, 2003, Pages 357-379

  Liu, Yi ^a   Fung, Pascale ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA STRUCTURES; DECISION THEORY; DECODING; DYNAMIC PROGRAMMING; MARKOV PROCESSES; NATURAL LANGUAGE PROCESSING SYSTEMS; SPEECH ANALYSIS;

ACOUSTIC MODEL RECONSTRUCTION; MANDARIN SPEECH RECOGNITION; PARTIAL CHANGE PHONE MODEL; PARTIAL PRONUNCIATION VARIATIONS; SPONTANEOUS SPEECH RECOGNITION;

CONTINUOUS SPEECH RECOGNITION;

EID: 0141975238     PISSN: 08852308     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0885-2308(03)00008-1     Document Type: Article

References (25)

1. Byrne, W., Finke, M., Khudanpur, S., Mcdonough, J., Nock, H., Riley, M., Saraclar, M., Wooters, C., Zavaliagkos, G., 1998. Pronunciation modelling using a hand-labelled corpus for conversational speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Settle, USA, pp. 313-316.
2. Byrne, W., Venkataramani, V., Kamm, T., Zheng, F., Fung, P., Liu, Y., Ruhi, U., 2001. Automatic generation of pronunciation lexicons for Mandarin spontaneous speech. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, USA.
3. Chen, F., 1990. Identification of contextual factors for pronunciation networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 753-756.
4. Eide, E., 1999. Automatic modeling of pronunciation variations. In: Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), Budapest, Hungary, pp. 451-454.
5. Finke, M., Fritsch, J., Koll, D., Waibel, A., 1999. Modeling and efficient decoding of large vocabulary conversational speech. In: Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), Budapest, Hungary, pp. 467-470.
6. Fosler-Lussier, E., 1999. Dynamic pronunciation models for automatic speech recognition. Ph.D. Thesis. International Computer Science Institute, Berkeley, CA.
7. Fokada, T., Sagisaka, Y., 1997. Automatic generation of a pronunciation dictionary based on a pronunciation network. In: Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), Rhodes, Greece, pp. 2471-2474.
8. Fung, P., Byrne, W., Zheng, F., Kamm, T., Liu, Y., Song, Z., Venkataramani, V., Ruhi, U., 2000. Pronunciation modeling of Mandarin casual speech. Final Report, The Johns Hopkins University Summer Workshop.
9. Hain, T., Woodland, P.C., 1999. Dynamic HMM selection for continuous speech recognition. In: Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), Budapest, Hungary, pp. 1327-1330.
10. Holter, T., Svendsen, T., 1999. Maximum likelihood modeling of pronunciation variation. Speech Communication 29, 177-191.
11. Kenstowicz, A., 1994. Phonology in generative grammar. Blackwell, Oxford.
12. Kessens, J.M., Wester, M., Strik, H., 1999. Improving the performance of a Dutch CSR by modeling within-word and cross-word pronunciation variation. Speech Communication 29, 193-207.
13. Li, A., Zheng, F., Byrne, W., Fung, P., Kamm, T., Liu, Y., Song, Z., Ruhi, U., Venkataramani, V., Chen, X., 2000. CASS: a phonetically transcribed corpus of Mandarin spontaneous speech. In: Proceedings of the International Conference on Spoken Language Processing (ICSLP), Beijing, China.
14. Liu, Y., Fung, P., 1999. Decision tree-based triphones are robust and practical for Mandarin speech recognition. In: Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), Budapest, Hungary, pp. 895-898.
15. Liu, Y., 2002. Pronunciation modeling for spontaneous Mandarin speech recognition. Ph.D. Thesis, The Hong Kong University of Science and Technology.
16. Luo, X., Jelinek, F., 1999. Probabilistic classification of HMM states for large vocabulary continuous speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, USA, pp. 353-356.
17. Rabiner, L., Juang, B.H., 1993. Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs, NJ.
18. Riley, M., Byrne, W., Finker, M., Khudanpur, S., Ljolje, A., Mcdonough, J., Nock, H., Saraclar, M., Wooters, C., Zavaliagkos, G., 1999. Stochastic pronunciation modelling from hand-labelled phonetic corpura. Speech Communication 29, 209-224.
19. Riley, M., Ljolje, A., 1995. Automatic generation of detailed pronunciation lexicions. In: Automatic Speech and Speaker Recognition: Advanced Topics. Kluwer Academic Press, Boston, pp. 285-302 (Chapter 12).
20. Saraclar, M., Nock, H., Khudanpur, S., 2000a. Pronunciation modeling by sharing Gaussian densities across phonetic models. Computer Speech and Language 14, 137-160.
21. Saraclar, M., Khudanpur, S., 2000b. Pronunciation ambiguity vs pronunciation variability in speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1679-1682.
22. Saraclar, M., 2000c. Pronunciation modeling for conversational speech recognition. Ph.D. Thesis, The Johns Hopkins University, Baltimore, MD.
23. Sloboda, T., Waibel, A., 1996. Dictionary learning for spontaneous speech recognition. In: Proceedings of the International Conference on Spoken Language Processing (ICSLP), Philadelphia, USA, pp. 2328-2331.
24. Strik, H., Cucchiarini, C., 1999. Modeling pronunciation variation for ASR: a survey of the literature. Speech Communication 29, 225-246.
25. Young, S., 1999. The HTK Book. Entropic Cambridge Research Laboratory.