Voice retrieval of Mandarin broadcast news speech

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 20, Issue 1, 2006, Pages 91-109

  Chen, Berlin ^a  

^a NATIONAL TAIWAN NORMAL UNIVERSITY   (Taiwan)

Author keywords

Broadcast news; Information retrieval; Multimedia; Multiscale indexing; Speech recognition

Indexed keywords

BROADCAST NEWS; MANDARIN BROADCAST; MULTISCALE INDEXING; VOICE RETRIEVAL;

BROADCASTING; COMPUTER APPLICATIONS; FORMAL LANGUAGES; INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; PERSONAL DIGITAL ASSISTANTS; RADIO TRANSMISSION; SOFTWARE PROTOTYPING; SPEECH RECOGNITION;

SPEECH;

EID: 33644999048     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001406004521     Document Type: Article

References (32)

1. X. L. Aubert, An overview of decoding techniques for large vocabulary continuous speech recognition, Comput. Speech Lang. 16 (2002) 89-114.
2. M. Bacchiani and B. Roark, Unsupervised language model adaptation, in Proc. IEEE Int. Conf. Acoustics. Speech. Signal Processing I (2003), pp. 224-227.
3. J. R. Bellegarda, Statistical language model adaptation: review and perspectives, Speech Commun. 42 (2004) 93-108.
4. P. Beyerlein, X. Aubert, R. Haeb-Umbach, M. Harris, D. Klakow, A. Wendemuth, S. Molau, H. Ney, M. Pitz and A. Sixtus, Large vocabulary continuous speech recognition of broadcast news - the Philips/RWTH approach, Speech Commun. 37 (2002) 109-131.
5. E. Chang, F. Seide, H. Meng, Z. Chen, Y. Shi and Y. G. Li, A system for spoken query information retrieval on mobile devices, IEEE Trans. Speech Aud. Process. 10(5) (2002) 531-541.
6. B. Chen, Exploring the use of latent topical information for statistical Chinese spoken document retrieval, Patt. Recogn. Lett. 27(1) (2006) 9-18.
7. B. Chen, J. W. Kuo and W. H. Tsai, Lightly supervised and data-driven approaches to mandarin broadcast news transcription, Int. J. Comput. Linguist. Chinese, Lang. Process. 10(1) (March 2005) 1-18.
8. B. Chen, W. H. Tsai and J. W. Kuo, Statistical language model adaptation for Mandarin broadcast news transcription, Proc. Int. Symp. Chinese Spoken Language Processing (2004), pp. 313-316.
9. B. Chen, H. M. Wang and L. S. Lee, Discriminating capabilities of syllablebased features and approaches of utilizing them for voice retrieval of speech information in Mandarin Chinese, IEEE Trans. Speech And. Process. 10(5) (2002) 303-314.
10. B. Chen, H. M. Wang and L. S. Lee, A discriminative HMM/N-gram-based retrieval approach for Mandarin spoken documents, ACM Trans. Asian Lang. Inform. Process. 3(2) (2004) 128-145.
11. S. F. Chen and J. Goodman, An empirical study of smoothing techniques for language modeling, Comput. Speech Lang. 13 (1999) 359-394.
12. A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. B 39(1) (1977) 1-38.
13. G. Evermann and P. C. Woodland, Design of fast LVCSR systems, Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (2003), pp. 7-12.
14. M. Federico and N. Bertoldi, Broadcast news LM adaptation using cotemporary texts, Proc. European Conf. Speech Communication and Technology 1 (2001), pp. 239-342.
15. S. Furui, T. Kikuchi, Y. Shinnaka and C. Hori, Speech-to-text and speech-to-speech summarization of spontaneous speech, IEEE Trans. Speech Aud. Process. 12(4) (2004) 401-408.
16. M. J. F. Gales and P. C. Woodland, Mean and variance adaptation within the MLLR framework, Comput. Speech Lang. 10 (1996) 249-264.
17. D. Gibbon and L. Begeja, Multimedia, processing for enhanced information delivery on mobile devices, Proc. MobEA (2004).
18. D. Harman, Overview of the fourth text retrieval conference (TREC-4), Proc. Fourth Text Retrieval. Conf. (1995), pp. 1-23.
19. F. Jelinek, B. Merialdo, S. Roukos and M. Strauss, A dynamic language model for speech recognition, Proc. Speech and Natural Language DARPA Workshop (1991), pp. 293-295.
20. B. H. Juang and S. Furui, Automatic recognition and understanding of spoken language - a first step toward natural human-machine communication, Proc. IEEE 88(8) (2000) 1142-1165.
21. T. Kemp and A. Waibel, Unsupervised training of a speech recognizer: recent experiments, Proc. European Conf. Speech Communication and Technology 6 (1999), pp. 2725-2728.
22. R. Kneser and H. Ney. Improved backing-off for M-gram language modeling, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing I (1995), pp. 181-184.
23. L. Lamel, J. L. Gauvain and G. Adda, Lightly supervised and unsupervised acoustic model training, Comput. Speech Lang. 16(1) (2002) 115-229.
24. L. S. Lee, Voice dictation of Mandarin Chinese, IEEE Sign. Process. Mag. 14(4) (1997) 63-101.
25. L. S. Lee and B. Chen, Spoken document understanding and organization, IEEE Sign. Process. Mag. 22(5) (2005) 42-60.
26. L. Nguyen and B. Xiang, Light supervision in acoustic model training, Proc. IEEE Int. Conf. Acoustics. Speech. Signal Processing I (2004), pp. 185-188.
27. S. Ortmanns, H. Ney and X. Aubert, A word graph algorithm for large vocabulary continuous speech recognition, Comput. Speech Lang. 11 (1997) 43-72.
28. G. Saon and M. Padmanabhan, Data-driven approach to designing compound words for continuous speech recognition, IEEE Trans. Speech Aud. Process. 9(4) (2001) 327-332.
29. A. Singhal, F. Pereira, Document expansion for speech retrieval, Proc. ACM SIGIR Conf. R&D in Information Retrieval (1999), pp. 34-41.
30. A. Stolcke, SRI Language Modeling Toolkit, version 1.3.3, 2000. http://www.speech.sri.com/projects/srilm/.
31. C. J. Wang, B. Chen and L. S. Lee, Improved Chinese spoken document retrieval with hybrid modeling and data-driven indexing features, Proc. Int. Conf. Spoken Language Processing (2002), pp. 1985-1988.
32. F. Wessel and H. Ney, Unsupervised training of acoustic models for large vocabularycontinuous speech recognition, Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (2001), pp. 307-310.