| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Outline of Final Research Achievements |
This research project focuses on group discussion for a problem solving and develops a framework for analyzing the group decision making process based on verbal and nonverbal (multimodal) information which observed from group members.We defined the quality of group output as an index set of social science (“product dimension”), which proposed by Hackman. The annotation data of the quality of group output has been collected. The machine learning model it developed to predict the product dimension from multimodal information including dialog transcription, head motion, speech prosody and turn taking. Novel co-occurrence data mining is proposed to capture the group interaction and multimodal patterns. Through the machine learning modeling and data mining,the specific multimodal features observed in group discussion process with high/low quality can be discovered automatically. Best prediction accuracy of product dimension is 82 % in binary classification task (high or low of quality).
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