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.

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In: 8th Annual Conference of the International Speech Communication Association

Publisher: International Speech Communication Association
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