tailieunhanh - Báo cáo hóa học: " Predicting user mental states in spoken dialogue systems"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Predicting user mental states in spoken dialogue systems | Callejas et al. EURASIP Journal on Advances in Signal Processing 2011 2011 6 http content 2011 1 6 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Predicting user mental states in spoken dialogue systems Zoraida Callejas1 David Griol2 and Ramón Lopez-Cozar1 Abstract In this paper we propose a method for predicting the user mental state for the development of more efficient and usable spoken dialogue systems. This prediction carried out for each user turn in the dialogue makes it possible to adapt the system dynamically to the user needs. The mental state is built on the basis of the emotional state of the user and their intention and is recognized by means of a module conceived as an intermediate phase between natural language understanding and the dialogue management in the architecture of the systems. We have implemented the method in the UAH system for which the evaluation results with both simulated and real users show that taking into account the user s mental state improves system performance as well as its perceived quality. Introduction In human conversation speakers adapt their message and the way they convey it to their interlocutors and to the context in which the dialogue takes place. Thus the interest in developing systems capable of maintaining a conversation as natural and rich as a human conversation has fostered research on adaptation of these systems to the users. For example Jokinen 1 describes different levels of adaptation. The simplest one is through personal profiles in which the users make static choices to customize the interaction . whether they want a male or female system s voice which can be further improved by classifying users into preferences groups. Systems can also adapt to the user environment as in the case of Ambient Intelligence applications 2 . A more sophisticated approach is to adapt the system to the user specific knowledge and expertise in which case