Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System

Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000

Volumn , Issue , 2000, Pages 722-728

  Litman, Diane J ^a   Pan, Shimei ^b  

^a AT AND T LABS RESEARCH   (United States)

^b Columbia University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; SPEECH PROCESSING;

ADAPTIVE VERSIONS; DESIGN AND EVALUATIONS; DIALOGUE STRATEGY; EMPIRICAL EVALUATIONS; SPOKEN DIALOGUE SYSTEM; SYSTEMS PERFORMANCE; TRAIN SCHEDULE;

SPEECH RECOGNITION;

EID: 85158142477     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Chu-Carroll, J., and Nickerson, J. S. 2000. Evaluating automatic dialogue strategy adaptation for a spoken dialogue system. In Proc. 1st Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
2. Chu-Carroll, J. 2000. MIMIC: An adaptive mixed initiative spoken dialogue system for information queries. In Proc. Applied Natural Language Processing (ANLP).
3. Cohen, W. 1996. Learning trees and rules with set-valued features. In Proc. 13th National Conference on Artificial Intelligence (AAAI).
4. Danieli, M., and Gerbino, E. 1995. Metrics for evaluating dialogue strategies in a spoken language system. In Proc. AAAI Spring Symposium on Empirical Methods in Discourse Interpretation and Generation.
5. Kamm, C.; Narayanan, S.; Dutton, D.; and Ritenour, R. 1997. Evaluating spoken dialog systems for telecommunication services. In Proc. European Conference on Speech Communication and Technology (EUROSPEECH).
6. Krahmer, E.; Swerts, M.; Theune, M.; and Weegels, M. 1999. Error spotting in human-machine interactions. In Proc. European Conference on Speech Communication and Technology (EUROSPEECH).
7. Levow, G.-A. 1998. Characterizing and recognizing spoken corrections in human-computer dialogue. In Proc. 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics (COLING-ACL).
8. Litman, D. J., and Pan, S. 1999. Empirically Evaluating an Adaptable Spoken Dialogue System. In Proc. 7th International Conference on User Modeling (UM).
9. Litman, D.; Hirschberg, J.; and Swerts, M. 2000. Predicting automatic speech recognition performance using prosodic cues. In Proc. 1st Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
10. Litman, D. J.; Walker, M. A.; and Kearns, M. J. 1999. Automatic detection of poor speech recognition at the dialogue level. In Proc. 37th Annual Meeting of the Association for Computational Linguistics (ACL).
11. Smith, R. W. 1998. An evaluation of strategies for selectively verifying utterance meanings in spoken natural language dialog. International Journal of Human-Computer Studies 48:627-647.
12. van Zanten, G. V. 1999. User modelling in adaptive dialogue management. In Proc. European Conference on Speech Communication and Technology (EUROSPEECH).
13. Walker, M.; Langkilde, I.; Wright, J.; Gorin, A.; and Litman, D. 2000. Learning to predict problematic situations in a spoken dialogue system: Experiments with how may i help you? In Proc. 1st Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
14. Walker, M.; Fromer, J.; and Narayanan, S. 1998. Learning optimal dialogue strategies: A case study of a spoken dialogue agent for email. In Proc. 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics (COLING-ACL).
15. Zeljkovic, I. 1996. Decoding optimal state sequences with smooth state likelihoods. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP).