Topic verification for dialogue systems based on Bayesian hypothesis testing

A novel approach to the task of verifying the uttered topic for simple dialogue systems is presented. Confidence measures generated by in-parallel topic-adapted automatic speech recognisers are used. Recognition performance, identification of user intention and detection of user-initiated topic change are greatly enhanced, most particularly for difficult topics, such as proper names or confirmations.