A spoken dialogue system with situation and emotion detection based on anthropomorphic learning for warming healthcare d

This work presents a spoken dialogue system with situation and emotion detection based on anthropomorphic learning for warming healthcare. To provide more warming feedback of the system, we combine situation and emotion detection with spoken dialogue system. Situation and emotion detection are based on lexical category using Partial-Matching Spoken Sentence Retrieval (PMSSR). Moreover, an anthropomorphic learning mechanism is proposed to improve the performance of emotion and situation detection. The mechanism based on out-of-vocabulary (OOV) detection is used to update emotion and situation database with new lexicon through interaction with user and internet. The experimental results show that the anthropomorphic learning mechanism increases the accuracy rate of situation and emotion detection by 30% and 20%, respectively.