Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Confidence Measures for Voice Search Applications

Ye-Yi Wang, Dong Yu, Yu-Cheng Ju, Geoffrey Zweig, and Alex Acero

Abstract

Voice search is the technology underlying many spoken dialog applications that enable users to access information using spoken queries. This paper reviews voice search technology, and proposes a new and effective method for computing semantic confidence measures. It explores the use of maximum entropy classifiers as confidence models, and investigates a feature selection algorithm that leads to an effective subset of prominent features for the classifier. The experimental results on a directory assistance application show that the reduced feature set not only makes the model more effective in handling different recognition and search engine combinations, but also results in a very informative confidence measure that is closely correlated with the actual voice search accuracy.

Details

Publication typeInproceedings
Published in8th Annual Conference of the International Speech Communication Association
Pages2721-2724
AddressAntwerp, Belgium
PublisherInternational Speech Communication Association
> Publications > Confidence Measures for Voice Search Applications