Spoken Language Understanding Method Using Confidence Measure and Dialogue History

摘要

In the real environment, it is hard for a speech recognizer to avoid misrecognitions completely. However, if misrecognitions occur, user's intentions are usually misunderstood by a conventional language understanding technique, which simply gives priority to the higher rank hypothesis of a speech recognition result (N-best). The utterances in a dialogue are coherent and correct user's intentions might appear in the lower rank hypothesis of N-best. To understand user's speech intentions in the real environment, we propose the language understanding technique that utilizes the dialogue context and confidence measure, which is the word posterior probability. The experimental results show that proposed technique is more efficient (about 15%) than the conventional technique.