International Journal of Human-Computer Studies
Empirically evaluating an adaptable spoken dialogue system
UM '99 Proceedings of the seventh international conference on User modeling
Computational models of the prosody/syntax mapping for spoken language systems
Computational models of the prosody/syntax mapping for spoken language systems
Characterizing and recognizing spoken corrections in human-computer dialogue
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic detection of poor speech recognition at the dialogue level
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Decoding optimal state sequence with smooth state likelihoods
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Emotions, speech and the ASR framework
Speech Communication - Special issue on speech and emotion
Using Dialogue Features to Predict Trouble During Collaborative Learning
User Modeling and User-Adapted Interaction
Predicting user reactions to system error
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Identifying user corrections automatically in spoken dialogue systems
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Labeling corrections and aware sites in spoken dialogue systems
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Exceptionality and natural language learning
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
ASR for emotional speech: Clarifying the issues and enhancing performance
Neural Networks - Special issue: Emotion and brain
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
User simulations for context-sensitive speech recognition in spoken dialogue systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Response-based confidence annotation for spoken dialogue systems
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
N-best rescoring based on pitch-accent patterns
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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In spoken dialogue systems, it is important for a system to know how likely a speech recognition hypothesis is to be correct, so it can reprompt for fresh input, or, in cases where many errors have occurred, change its interaction strategy or switch the caller to a human attendant. We have discovered prosodic features which more accurately predict when a recognition hypothesis contains a word error than the acoustic confidence score thresholds traditionally used in automatic speech recognition. We present analytic results indicating that there are significant prosodic differences between correctly and incorrectly recognized turns in the TOOT train information corpus. We then present machine learning results showing how the use of prosodic features to automatically predict correct versus incorrectly recognized turns improves over the use of acoustic confidence scores alone.