Name
Title | ||
|---|---|---|
A method using acoustic features to detect inadequate utterances in medical communication |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICSMC.2012.6377686 | Systems, Man, and Cybernetics |
Keywords | Field | DocType |
acoustic analysis,behavioural sciences computing,biomedical communication,feature extraction,support vector machines,SVM,acoustic features,doctor inadequate utterance detection,facial expression,gestures,grammatical features,medical communication,mental states,nonverbal information,support vector machine,voice tone,Acoustic Features,Mental State,Support Vector Machine | Computer science,Gesture,Support vector machine,Nonverbal communication,Speech recognition,Feature extraction,Facial expression,Artificial intelligence,Natural language processing,Biomedical communication,Recall,Mental state | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-4673-1712-2 | 1 |
PageRank | References | Authors |
0.48 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kurisu, M. | 1 | 1 | 0.48 |
| Mera, K. | 2 | 2 | 1.11 |
| Wada, R. | 3 | 1 | 0.48 |
| Kurosawa, Y. | 4 | 4 | 0.90 |