Query modeling for spoken document retrieval

2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings

Volumn , Issue , 2011, Pages 389-394

  Chen, Berlin ^a   Chen, Pei Ning ^a   Chen, Kuan Yu ^b  

^a NATIONAL TAIWAN NORMAL UNIVERSITY   (Taiwan)

^b INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION NEED; LANGUAGE MODELING; MODELING TECHNIQUE; QUERY FORMULATION; RETRIEVAL FRAMEWORKS; RETRIEVAL METHODS; SPEECH INFORMATION; SPOKEN DOCUMENT; SPOKEN DOCUMENT RETRIEVAL; TOPIC DETECTION AND TRACKING;

COMPUTATIONAL LINGUISTICS; SPEECH RECOGNITION;

INFORMATION RETRIEVAL;

EID: 84858957387     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASRU.2011.6163963     Document Type: Conference Paper

References (25)

1. L. S. Lee and B. Chen, "Spoken document understanding and organization," IEEE Signal Processing Magazine, 22 (5), pp. 42-60, 2005. (Pubitemid 41488520)
2. M. Ostendorf, "Speech technology and information access," IEEE Signal Processing Magazine, 25(3), pp. 150-152, 2008.
3. C. Chelba, T. J. Hazen, and M. Saraclar, "Retrieval and browsing of spoken content," IEEE Signal Processing Magazine, 25 (3), pp. 39-49, 2008. (Pubitemid 351695639)
4. B. Chen, "Word topic models for spoken document retrieval and transcription," ACM Transactions on Asian Language Information Processing, 8 (1), pp. 2:1-2:27, 2009.
5. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technology behind Search, ACM Press, 2011.
6. T. K. Chia, K. C. Sim, H. Li, and H. T. Ng, "Statistical lattice-based spoken document retrieval," ACM Transactions on Information Systems, 28 (1), pp. 2:1-2:30, 2010.
7. V. T. Turunen and M. Kurimo, "Indexing confusion networks for morph-based spoken document retrieval," in Proc. SIGIR 2007.
8. J. M. Ponte and W. B. Croft, "A language modeling approach to information retrieval," in Proc. SIGIR 1998.
9. C. X. Zhai, Statistical Language Models for Information Retrieval: A Critical Review, Foundations and Trends in Information Retrieval, 2 (3), 137-213, 2008.
10. D. M. Blei et al., "Latent Dirichlet allocation," Journal of Machine Learning Research, 3, pp. 993-1022, 2003.
11. T. Hoffmann, "Unsupervised learning by probabilistic latent semantic analysis," Machine Learning, 42, pp. 177-196, 2001.
12. D. Blei and J. Lafferty, "Topic models," in A. Srivastava and M. Sahami, (eds.), Text Mining: Theory and Applications. Taylor and Francis, 2009.
13. X. Yi and J. Allan, "A Comparative Study of Utilizing Topic Models for Information Retrieval," in Proc. ECIR 2009.
14. P.-N. Chen, K.-Y. Chen, B. Chen, "Leveraging relevance cues for improved spoken document retrieval," in Proc. Interspeech 2011.
15. X. Wei and W. B. Croft, "LDA-based document models for ad-hoc retrieval," in Proc. SIGIR 2006.
16. S.-H. Lin, Y.-M. Yeh, B. Chen, "Leveraging Kullback-Leibler divergence measures and information-rich cues for speech summarization," IEEE Transactions on Audio, Speech and Language Processing, 19(4), pp. 871-882, 2011.
17. V. Lavrenko and W. B. Croft, "Relevance-based language models," in Proc. SIGIR 2001.
18. V. Lavrenko, A Generative Theory of Relevance, Springer, 2009.
19. Y. Lv and C. X. Zhai, "A comparative study of methods for estimating query language models with pseudo feedback," in Proc. CIKM 2009.
20. K.-Y. Chen and B. Chen, "Relevance language modeling for speech recognition," in Proc. ICASSP 2011.
21. X. Wang, H. Fang, and C. X. Zhai, "A study of methods for negative relevance feedback," in Proc. SIGIR 2008.
22. N. Soskin, O. Kurland and C. Domshlak, "Navigating in the dark: Modeling uncertainty in ad hoc retrieval using multiple relevance models," in Proc. ICTIR 2009.
23. LDC. 2000. Project topic detection and tracking. Linguistic Data Consortium. http://www.ldc.upenn.edu/Projects/TDT/.
24. J. W. Kuo, B. Chen, "Minimum word error based discriminative training of language models," in Proc. Interspeech 2005.
25. B. Chen and S. -H. Lin, "A risk-aware modeling framework for speech summarization," IEEE Transactions on Audio, Speech and Language Processing, 2011.