Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
Kazunori Komatani,Alexander I. Rudnicky +1 more
- 04 Aug 2009
- pp 89-92
TL;DR: This work combines the estimated ASR accuracy with the user's barge-in rate, which represents how well the user is accustomed to using the system, to predict interpretation errors in barge in utterances.
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Abstract: Modeling of individual users is a promising way of improving the performance of spoken dialogue systems deployed for the general public and utilized repeatedly. We define "implicitly-supervised" ASR accuracy per user on the basis of responses following the system's explicit confirmations. We combine the estimated ASR accuracy with the user's barge-in rate, which represents how well the user is accustomed to using the system, to predict interpretation errors in barge-in utterances. Experimental results showed that the estimated ASR accuracy improved prediction performance. Since this ASR accuracy and the barge-in rate are obtainable at runtime, they improve prediction performance without the need for manual labeling.
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Citations
•Proceedings Article
Handling User Interruptions in an Embodied Conversational Agent
Stephen Pulman
- 01 Jan 2010
TL;DR: An approach to handling user barge-in interruptions in conversations with an Embodied Conversational Agent is outlined and its implementation in the Companions English demonstrator is described.
Generating context−sensitive ECA responses to user barge−in interruptions
Nigel T. Crook,Debora Field,Cameron Smith,Sue Harding,Stephen Pulman,Marc Cavazza,Daniel Charlton,Roger K. Moore,Johan Boye +8 more
TL;DR: An Embodied Conversational Agent that incorporates a context-sensitive mechanism for handling user barge-in and the affective ECA is designed to recognise and be empathetic to the emotional state of the user.
•Proceedings Article
Online Error Detection of Barge-In Utterances by Using Individual Users' Utterance Histories in Spoken Dialogue System
Kazunori Komatani,Hiroshi G. Okuno +1 more
- 24 Sep 2010
TL;DR: A method to detect erroneous interpretation results of user utterances by exploiting utterance histories of individual users in spoken dialogue systems that were deployed for the general public and repeatedly utilized is developed.
Interpretation and generation incremental management in natural interaction systems
TL;DR: An incremental approach, in which the interpretation of contributions is done as they take place, and the final generated contributions are the result of constant rectifications, reformulations and cancellations of the initially formulated contributions.
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TESIS DOCTORAL Gestión Avanzada de Turnos para la Interacción Natural
David del Valle Agudo,Francisco Javier Calle Gómez,María Dolores Cuadra Fernández +2 more
- 01 Jan 2012
TL;DR: In the context of the Sistemas de Interaccion Natural (SIN), the authors define a set of rules for the toma de turno in the interaccion humana.
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References
•Proceedings Article
Doing Research on a Deployed Spoken Dialogue System: One Year of Let's Go! Experience
Antoine Raux,Dan Bohus,Brian Langner,Alan W. Black,Maxine Eskenazi +4 more
- 01 Jan 2006
TL;DR: Let’s Go, a telephonebased bus schedule information system that has been in use by the Pittsburgh population since March 2005, and results show that while task success correlates strongly with speech recognition accuracy, other aspects of dialogue such as turn-taking, the set of error recovery strategies, and the initiative style also significantly impact system performance and user behavior.
•Proceedings Article
Learning to predict problematic situations in a spoken dialogue system: experiments with how may I help you?
Marilyn A. Walker,Irene Langkilde,Jerry H. Wright,Allen Louis Gorin,Diane J. Litman +4 more
- 29 Apr 2000
TL;DR: Results on learning to automatically identify and predict problematic human-computer dialogues in a corpus of 4774 dialogues collected with the How May I Help You spoken dialogue system are reported.
116
Automatic Detection of Poor Speech Recognition at the Dialogue Level
Diane J. Litman,Marilyn A. Walker,Michael S. Kearns +2 more
- 20 Jun 1999
TL;DR: This paper adopts a machine learning approach to learn rules from a dialogue corpus for identifying situations where the speech recognizer is performing poorly and shows a significant improvement over the baseline.
Noise robust real world spoken dialogue system using GMM based rejection of unintended inputs.
Akinobu Lee,Keisuke Nakamura,Ryuichi Nisimura,Hiroshi Saruwatari,Kiyohiro Shikano +4 more
- 04 Oct 2004
TL;DR: ICSLP2004: the 8th International Conference on Spoken Language Processing, October 4-8, 2004, Jeju Island, Korea.
64
User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance
TL;DR: This work addresses the issue of appropriate user modeling to generate cooperative responses to users in spoken dialogue systems and proposes more generalized modeling that is automatically derived by decision tree learning using actual dialogue data collected by the system.
57