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. Index Terms: voice search, directory assistance, confidence measure, Tf-Idf vector space model, maximum entropy model. 1.

are those of the author and should not be interpreted as representing the official policies, either express or implied, of any sponsoring institution, the U.S. government, or any other entity. Keywords: spoken dialog systems, conversational spoken language interfaces, error detection, error recovery strategies, error recovery policies, dialog management, RavenClaw, implicitly-supervised One of the most important and persistent problems in the development of conversational spoken language interfaces is their lack of robustness when confronted with understanding-errors. Most of these errors stem from limitations in current speech recognition technology, and, as a result, appear across all domains and interaction types. There are two approaches towards increased robustness: prevent the errors from happening, or recover from them through conversation, by interacting with the users. In this dissertation we have engaged in a research program centered on the second approach. We argue that three capabilities are needed in order to seamlessly and efficiently recover from errors: (1) systems must be able to detect the errors, preferably as soon as they happen, (2) systems must be equipped with a rich repertoire of error recovery strategies that can be used to set the conversation back on track, and (3) systems must know how to choose optimally between different recovery

[Interpreting the signs given by a speech signal]