Affective computing
Infant-like social interactions between a robot and a human caregiver
Adaptive Behavior
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Human interaction based reasoning using ontology alignment
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Virtual Doctor System (VDS): Framework on Reasoning issues
Proceedings of the 2010 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the 9th SoMeT_10
Virtual doctor system (VDS): medical decision reasoning based on physical and mental ontologies
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Semantical case based reasoning related to virtual doctor system (VDS)
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
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To implement listener estimate emotion in speech, we propose an approach based on estimation emotion in speaker's speech. First, we use training data consist of speech data and emotion estimated by a listener. We collect human speech data and synthesize speech using speech synthesize technique. Next, we get syllabic features from training data using speech synthesize. We can divide speech into phonemes using speech synthesize. After getting phonemes, we make syllables based on phonemes. We get the fundamental frequency, power and time of each phoneme and calculate the statistics values and the inclination of the regression of them. Next, we make classifiers from these values. Finally, we estimate emotion using them. To evaluate our approach, we did the experiment. The experimental result does not say our approach is strong to do our goal. It shows some points we should modify to enhance our approach. Future works of our research are as follows. We collect training data using speech synthesize. We reconsider speech features for estimation of emotion and make classifiers using them. In addition, we divide emotion into s detail by features before making classifiers. And we evaluate the new approach. We will also modify our approaches to use in real-time.