This paper describes new feature parameters for detecting misunderstandings in a spoken dialogue system. Although recognition errors cannot be completely avoided with current speech recognition techniques, a spoken dialogue system could be a good human-machine interface if it could automatically detect and recover from its own misunderstandings during natural interaction between it and a user. For this purpose, we collected user responses to system confirmations with/without the system misunderstandings using the wizard of OZ method so that we could analyze the differences in the characteristics of user responses following correct/incorrect confirmations. The experimental results demonstrate that the content and duration of the user responses are good feature parameters for detecting the system's misunderstandings.
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