Itoh Y. An understanding strategy based on plausibility score in recognition history using CSR confidence measure

Although car-navigation systems attract attention as one of spoken dialogue interfaces, recognition errors due to the influence of natural speech and surrounding noise may prevent a smooth di-alogue and disappoint the user. Thus, this research aims at the construction of a dialogue system which can achieve a smooth di-alogue and a high degree of user satisfaction. Our system per-forms language understanding and response generation by using the confidence measure(CM) based on continuous speech recog-nizer(CSR) and the recognition history. This paper shows the spo-ken language understanding technique in the dialogue system. The CM, together with the speech type and the recognition history, is used for generating an integrated score. The system realizes a spo-ken language understanding which is more plausible for a given dialogue. As the result of evaluation experiment, it was shown that our system is more efficient (more than 15%) than a language understanding technique which simply gives priority to the higher-rank hypothesis of a speech recognition result (n-best). 1