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  4. Design of an Emotion Model Based on Hidden Markov Model with Optimistic and Pessimistic Personalities for Emotional Speech Interaction
 
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Design of an Emotion Model Based on Hidden Markov Model with Optimistic and Pessimistic Personalities for Emotional Speech Interaction

Date Issued
2011
Date
2011
Author(s)
Li, Yun-Gang
URI
http://ntur.lib.ntu.edu.tw//handle/246246/253960
Abstract
Artificial emotion model is considered as a key factor to achieve a more effective and believable human-robot interaction. As the speech communication plays an essential part of our daily life, the utilization of the emotional speech is expected to make human-robot communication smooth. Thus, it is promising to design a robot which has perception of emotion in speech, and responses corresponding to internal emotion similar to human. Since the individual emotion exists as a part of an emotion network and they have certain probability to interact with other emotions, HMM (Hidden Markov Model) as a stochastic model is an appropriate way to describe transition process of emotions. Therefore, an emotion model based on HMM is used in this thesis. The model simulates the dynamic processes of emotional self-regulation and emotional transference under the influence of the same stimuli arouse by the result of emotional speech recognition. In addition, by selecting the parameters of the model, the models of optimistic and pessimistic personality traits are also built. However, since the theory of emotion definition is obscure and not well-established, the accuracy of emotional speech recognition in real life is limited. Consequently, the error of recognition will have impact influence on the characteristic of designed personalities. In order to maintain the consistency of the personality traits, the personality models under the influence of all the combinations of recognition error are considered and analyzed to figure out the relationship with model parameters through a series of simulation. At last, they were divided into groups to discuss with for simplification, and each group is modified by adjusting the corresponding model parameters within acceptable error requirement.
Subjects
emotional speech recognition
emotion model
hidden markov model
human-robot interaction
Type
thesis
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