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SummaryIn this publication experiences with commercial spoken dialogue systems are discussed and guidelines for achieving high usability are pointed out. Different from most commercially deployed IVR (Interactive Voice Response) systems, the systems discussed in this paper belong to a new generation of real mixed-initiative spoken dialogue systems, i. e., the user may take the initiative, using full sentences, at virtually any point in time during the dialogue. We use three commercially deployed systems as example applications: the automated switchboard of a large German company, the movie information system operated by Germany´s largest multiplex cinema, and a football Bundesliga information system operated by a German media company.

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Out-of-vocabulary words (OOVs) are often the main reason for the failure of tasks like automated voice searches or humanmachine dialogs. This is especially true if rare but task-relevant content words, e.g. person or location names, are not in the recognizer's vocabulary. Since applications like spoken dialog systems use the result of the speech recognizer to extract a semantic representation of a user utterance, the detection of OOVs as well as their (semantic) word class can support to manage a dialog successfully. In this paper we suggest to combine two wellknown approaches in the context of OOV detection: semantic word classes and OOV models based on sub-word units. With our system, which builds upon the widely used Kaldi speech recognition toolkit, we show on two different data sets that-compared to other methods-such a combination improves OOV detection performance for open word classes at a given false alarm rate. Another result of our approach is a reduction of the word error rate (WER).

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