Affective-cognitive dialogue act detection in an error-aware spoken dialogue system

This paper presents an approach to affective-cognitive dialogue act detection in a spoken dialogue. To achieve this goal, the input utterance is decoded as the affective state by an emotion recognizer and a word sequence by an imperfect speech recognizer separately. Besides, four types of evidences are employed to grade the score of each recognized word. The recognized word sequence is used to derive the candidate sentences to alleviate the problem of unexpected language usage for the cognitive state predicted by the vector space-based dialogue act detection. The Boltzmann selection based method is then employed to predict the next possible act in the spoken dialogue system according to the affective-cognitive states. A model of affective anticipatory reward that is assumed to arise from the emotional seeking system is adopted for enhancing the efficacy of dialogue act detection. Finally, the evaluation data are collected and the experimental results confirm the improved performance of the proposed approach compared to the baseline system on the task completion rate.