By Topic

Context-sensitive language modeling for large sets of proper nouns in multimodal 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

2 Author(s)
Gruenstein, A. ; Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA ; Seneff, S.

We explore several language modeling strategies for increasing the recognition accuracy among large sets of proper nouns in a map- based multimodal dialogue system which provides restaurant information. In particular, we evaluate several mechanisms for exploiting dialogue context, the two most promising of which involve a semi- static metropolitan-region based large set of proper nouns competing with a smaller, in-focus subset. We show that these techniques decrease word, concept, and proper noun error rates under several training conditions. We also present a technique to generalize sparse training data through derived templates to improve language model robustness.

Published in:

Spoken Language Technology Workshop, 2006. IEEE

Date of Conference:

10-13 Dec. 2006