N-best speech hypothesis reordering based on comprehensive information theory

This paper proposes a hypothesis reordering technique, based on a newly established theory, namely comprehensive information theory, to improve the accuracy of speech recognition in a man-machine dialog system. For each hypothesis, we calculate the amount of comprehensive information that hypothesis provided and then reorder N-best hypothesis according to the amount of comprehensive information. Results of experiments have shown its effectiveness.