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.
Bibliographic reference. Wang, Ye-Yi / Yu, Dong / Ju, Yun-Cheng / Zweig, Geoffrey / Acero, Alex (2007): "Confidence measures for voice search applications", In INTERSPEECH-2007, 2721-2724.