TY - GEN
T1 - Multi-Domain Spoken Dialogue System with Extensibility and Robustness against Speech Recognition Errors
AU - Komatani, Kazunori
AU - Kanda, Naoyuki
AU - Nakano, Mikio
AU - Nakadai, Kazuhiro
AU - Tsujino, Hiroshi
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
N1 - Publisher Copyright:
© 2006 Association for Computational Linguistics.
PY - 2006
Y1 - 2006
N2 - We developed a multi-domain spoken dialogue system that can handle user requests across multiple domains. Such systems need to satisfy two requirements: extensibility and robustness against speech recognition errors. Extensibility is required to allow for the modification and addition of domains independent of other domains. Robustness against speech recognition errors is required because such errors are inevitable in speech recognition. However, the systems should still behave appropriately, even when their inputs are erroneous. Our system was constructed on an extensible architecture and is equipped with a robust and extensible domain selection method. Domain selection was based on three choices: (I) the previous domain, (II) the domain in which the speech recognition result can be accepted with the highest recognition score, and (III) other domains. With the third choice we newly introduced, our system can prevent dialogues from continuously being stuck in an erroneous domain. Our experimental results, obtained with 10 subjects, showed that our method reduced the domain selection errors by 18.3%, compared to a conventional method.
AB - We developed a multi-domain spoken dialogue system that can handle user requests across multiple domains. Such systems need to satisfy two requirements: extensibility and robustness against speech recognition errors. Extensibility is required to allow for the modification and addition of domains independent of other domains. Robustness against speech recognition errors is required because such errors are inevitable in speech recognition. However, the systems should still behave appropriately, even when their inputs are erroneous. Our system was constructed on an extensible architecture and is equipped with a robust and extensible domain selection method. Domain selection was based on three choices: (I) the previous domain, (II) the domain in which the speech recognition result can be accepted with the highest recognition score, and (III) other domains. With the third choice we newly introduced, our system can prevent dialogues from continuously being stuck in an erroneous domain. Our experimental results, obtained with 10 subjects, showed that our method reduced the domain selection errors by 18.3%, compared to a conventional method.
UR - http://www.scopus.com/inward/record.url?scp=85015979450&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85015979450
T3 - SIGDIAL 2006 - 7th SIGdial Workshop on Discourse and Dialogue, Proceedings
SP - 9
EP - 17
BT - SIGDIAL 2006 - 7th SIGdial Workshop on Discourse and Dialogue, Proceedings
A2 - Alexandersson, Jan
A2 - Knott, Alistair
PB - Association for Computational Linguistics (ACL)
T2 - 7th SIGdial Workshop on Discourse and Dialogue, SIGDIAL 2006
Y2 - 15 July 2006 through 16 July 2006
ER -