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Adaptive language models for spoken dialogue systems

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5 Author(s)
Roger Argiles Solsona ; Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, U.S.A. ; Eric Fosler-Lussier ; Hong-Kwang J. Kuo ; Alexandras Potamianos
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In this paper, we investigate both generative and statistical approaches for language modeling in spoken dialogue systems. Semantic class-based finite state and n-gram grammars are used for improving coverage and modeling accuracy when little training data is available. We have implemented dialogue-state specific language model adaptation to reduce perplexity and improve the efficiency of grammars for spoken dialogue systems. A novel algorithm for combining state-independent n-gram and state-dependent finite state grammars using acoustic confidence scores is proposed. Using this combination strategy, a relative word error reduction of 12% is achieved for certain dialogue states within a travel reservation task. Finally, semantic class multigrams are proposed and briefly evaluated for language modeling in dialogue systems.

Published in:

Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on  (Volume:1 )

Date of Conference:

13-17 May 2002