A phonotactic-semantic paradigm for automatic spoken document classification

SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2005, Pages 369-376

  Ma, Bin ^a   Li, Haizhou ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

acoustic words; n gram; phonotactic semantic; semantic domain; spoken document classification; voice tokenizer

Indexed keywords

N-GRAM; PHONOTACTIC-SEMANTIC; SEMANTIC DOMAINS; SPOKEN DOCUMENT; TOKENIZER;

BENCHMARKING; INFORMATION RETRIEVAL; SEMANTICS;

INFORMATION RETRIEVAL SYSTEMS;

EID: 53849094167     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1076034.1076098     Document Type: Conference Paper

References (18)

1. Alshawi, H. Effective utterance classification with unsupervised phonotactic models. In Proceedings of HLT-NAACL, Edmonton, 2003, 1-7.
2. Bellegarda, J.R. Exploiting latent semantic information in statistical language modeling, In Proc. of the IEEE, 88, 8 (Aug. 2000), 1279-1296.
3. rd Annual Symposium on Document Analysis and Information Retrieval, 1994, 161-169.
4. Chu-Carroll, J., and Carpenter, B. Vector-based Natural Language Call Routing, Computational Linguistics, 25,3 (Sept. 1999), 361-388.
5. Dai, P., Iurgel, U., and Rigoll, G. A novel feature combination approach for spoken document classification with support vector machines, Multimedia Information Retrieval Workshop 2003, Toronto, Canada, Aug 2003.
6. Duda, R.O., and Hart, P.E. Pattern Classification and scene analysis. John Wiley & Sons, 1973.
7. Garofolo, J.S., Auzanne, C.G.P., and Voorhees, E.M. The TREC spoken document retrieval track: A success story. In Proceedings of the RIAO 2000 Conference: Context-based Multimedia Information Access, Paris 2000, 1-20.
8. Hieronymus, J.L. ASCII Phonetic Symbols for the World's Languages: Worldbet. Technical Report AT&T Bell Labs, 1994.
9. Ma, B., Li, H., and Lee, C.H. An Acoustic Segment Modeling Approach to Automatic Language Identification, submitted to Interspeech 2005.
10. Mladenic, D., Brank, J., Grobelnik, M., and Milic-Frayling, N. Feature selection using linear classifier weights: Interaction with classification with classification models, SIGIR'04, Sheffield, UK, 2004, 234-241
11. Muller, K.R., Mika, S., Ratsch, G., Tsuda, K. and Scholkopf, B. An introduction to kernel-based learning algorithm, IEEE Trans on Neural Networks, 12, 2 (Mar 2001), 181-202.
12. Ng, C., Wilkinson, R., and Zobel, J. Experiments in Spoken Document Retrieval using Phoneme N-gram, Speech Communication, 32 (2000), 61-77.
13. Ng, K., Zue, V.W. Subword unit representations for spoken document retrieval, In Proc. of Eurospeech 1997, Rhodes, Greece, 1607-1610.
14. Salton, G. The SMART Retrieval System. Prentice-Hall, Englewood Cliffs, NJ, 1971.
15. Singer, E., Torres-Carrasquillo, P.A., Gleason, T.P., Campbell W.M., and Reynolds, D.A. Acoustic, Phonetic and Discriminative Approaches to Automatic language recognition, In Proc. of Eurospeech, 2003.
16. Torres-Carrasquillo, P.A., Reynolds, D.A., and Deller. Jr., J.R. Language identification using Gaussian Mixture model tokenization. In Proc. of ICASSP, 2002.
17. Zipf, G.K. Human Behavior and the Principal of Least effort, an introduction to human ecology. Addison-Wesley, Reading, Mass, 1949.
18. Zissman, M.A. Comparison of four approaches to automatic language identification of telephone speech, IEEE Trans. on Speech and Audio Processing, 4, 1 (Jan. 1996), 31-44. (Pubitemid 126752987)