By Topic

Discriminative training of natural language call routers

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
$33 $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)
H. -K. J. Kuo ; IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA ; Chin-Hui Lee

This paper shows how discriminative training can significantly improve classifiers used in natural language processing, using as an example the task of natural language call routing, where callers are transferred to desired departments based on natural spoken responses to an open-ended "How may I direct your call?" prompt. With vector-based natural language call routing, callers are transferred using a routing matrix trained on statistics of occurrence of words and word sequences in a training corpus. By re-training the routing matrix parameters using a minimum classification error criterion, a relative error rate reduction of 10-30% was achieved on a banking task. Increased robustness was demonstrated in that with 10% rejection, the error rate was reduced by 40%. Discriminative training also improves portability; we were able to train call routers with the highest known performance using as input only text transcription of routed calls, without any human intervention or knowledge about what terms are important or irrelevant for the routing task. This strategy was validated with both the banking task and a more difficult task involving calls to operators in the UK. The proposed formulation is applicable to algorithms addressing a broad range of speech understanding, information retrieval, and topic identification problems.

Published in:

IEEE Transactions on Speech and Audio Processing  (Volume:11 ,  Issue: 1 )