Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Empirically evaluating an adaptable spoken dialogue system
UM '99 Proceedings of the seventh international conference on User modeling
How to find trouble in communication
Speech Communication - Special issue on speech and emotion
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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
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
Degrees of grounding based on evidence of understanding
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Predicting concept types in user corrections in dialog
SRSL '09 Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language
Improving a virtual human using a model of degrees of grounding
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
From annotator agreement to noise models
Computational Linguistics
Learning about voice search for spoken dialogue systems
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
The dynamics of action corrections in situated interaction
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Learning to balance grounding rationales for dialogue systems
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Data mining to support human-machine dialogue for autonomous agents
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Autonomous self-assessment of autocorrections: exploring text message dialogues
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machine-learning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70–28.99% to 15.72%.