TY - GEN
T1 - Prediction of Social Maladaptation using Emotional Entrainment of Disgust during Comprehensive Psychiatric Interviews
AU - Kenji, Yokotani
AU - Gen, Takagi
AU - Kobun, Wakashima
N1 - Publisher Copyright:
© 2020 APSIPA.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Previous speech entrainment studies have shown disagreement in their findings: One group emphasized that acoustic entrainment predicts social adaptation, whereas another group emphasized that it predicts social maladaptation. Our study aims to resolve the disagreement from the perspective of emotional entrainment: the entrainment of positive emotions predicts social adaptation, whereas the entrainment of negative emotions predicts social maladaptation. Using a machine-learned sentiment classifier, we estimated the probability of anger, disgust, fear, happiness, neutrality, and sadness in speech. The corpus consisted of dialogues recorded from 29 comprehensive mental health interviews. The Jensen- Shannon divergence was also calculated to estimate the (dis)entrainment. Results showed that the entrainment of happiness significantly demonstrated the rapport of the participants with their therapist. In contrast, their entrainment of disgust significantly demonstrated their social maladaptation. Our study observed social maladaptation to be contrastingly related to positive and negative emotional entrainment. Classification of speech from an emotional perspective could enrich the study of entrainment and facilitate the analysis of emotional communication.
AB - Previous speech entrainment studies have shown disagreement in their findings: One group emphasized that acoustic entrainment predicts social adaptation, whereas another group emphasized that it predicts social maladaptation. Our study aims to resolve the disagreement from the perspective of emotional entrainment: the entrainment of positive emotions predicts social adaptation, whereas the entrainment of negative emotions predicts social maladaptation. Using a machine-learned sentiment classifier, we estimated the probability of anger, disgust, fear, happiness, neutrality, and sadness in speech. The corpus consisted of dialogues recorded from 29 comprehensive mental health interviews. The Jensen- Shannon divergence was also calculated to estimate the (dis)entrainment. Results showed that the entrainment of happiness significantly demonstrated the rapport of the participants with their therapist. In contrast, their entrainment of disgust significantly demonstrated their social maladaptation. Our study observed social maladaptation to be contrastingly related to positive and negative emotional entrainment. Classification of speech from an emotional perspective could enrich the study of entrainment and facilitate the analysis of emotional communication.
KW - acoustic synchrony
KW - comprehensive mental health interview
KW - emotional entrainment
KW - machine-learned sentiment classifier
UR - http://www.scopus.com/inward/record.url?scp=85100950000&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100950000&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85100950000
T3 - 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
SP - 1001
EP - 1007
BT - 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Y2 - 7 December 2020 through 10 December 2020
ER -