By Topic

Adaptive language models for spoken dialogue systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Solsona, Roger Argiles ; Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, U.S.A. ; Fosler-Lussier, E. ; Kuo, Hong-Kwang J. ; Potamianos, Alexandras
more authors

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