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A method using acoustic features to detect inadequate utterances in medical communication

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5 Author(s)
Michihisa Kurisu ; Graduate School of Information Sciences, Hiroshima City University, Japan ; Kazuya Mera ; Ryunosuke Wada ; Yoshiaki Kurosawa
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We previously proposed a method that uses grammatical features to detect inadequate utterances of doctors. However, nonverbal information such as that conveyed by gestures, facial expression, and tone of voice are also important. In this paper, we propose a method that uses eight acoustic features to detect three types of mental states (sincerity, confidence, and doubtfulness/acceptance). A Support Vector Machine (SVM) is used to learn these features. Experiments showed that the system's accuracy and recall rates respectively ranged from 0.79-0.91 and 0.80-0.94.

Published in:

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Date of Conference:

14-17 Oct. 2012