TY - JOUR
T1 - Automatic assessment of individual culture attribute of power distance using a social context-enhanced prosodic network representation
AU - Tsai, Fu Sheng
AU - Yang, Hao Chun
AU - Chang, Wei Wen
AU - Lee, Chi Chun
N1 - Publisher Copyright:
© 2018 International Speech Communication Association. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Culture is a collective social norm of human societies that often influences a person's values, thoughts, and social behaviors during interactions at an individual level. In this work, we present a computational analysis toward automatic assessing an individual's culture attribute of power distance, i.e., a measure of his/her belief about status, authority and power in organizations, by modeling their expressive prosodic structures during social encounters with people of different power status. Specifically, we propose a center-loss embedded network architecture to jointly consider the effect of social interaction contexts on individuals' prosodic manifestations in order to learn an enhanced representation for power distance recognition. Our proposed prosodic network achieves an overall accuracy of 78.6% in binary classification task of recognizing high versus low power distance. Our experiment demonstrates an improved discrim-inability (17.6% absolute improvement) over prosodic neural network without social context enhancement. Further visualization reveals that the diversity in the prosodic manifestation for individuals with low power distance seems to be higher than those of high power distance.
AB - Culture is a collective social norm of human societies that often influences a person's values, thoughts, and social behaviors during interactions at an individual level. In this work, we present a computational analysis toward automatic assessing an individual's culture attribute of power distance, i.e., a measure of his/her belief about status, authority and power in organizations, by modeling their expressive prosodic structures during social encounters with people of different power status. Specifically, we propose a center-loss embedded network architecture to jointly consider the effect of social interaction contexts on individuals' prosodic manifestations in order to learn an enhanced representation for power distance recognition. Our proposed prosodic network achieves an overall accuracy of 78.6% in binary classification task of recognizing high versus low power distance. Our experiment demonstrates an improved discrim-inability (17.6% absolute improvement) over prosodic neural network without social context enhancement. Further visualization reveals that the diversity in the prosodic manifestation for individuals with low power distance seems to be higher than those of high power distance.
KW - Behavioral signal processing
KW - Center-loss embedding
KW - Culture attribute
KW - Power distance
KW - Prosody
UR - http://www.scopus.com/inward/record.url?scp=85054955560&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054955560&partnerID=8YFLogxK
U2 - 10.21437/Interspeech.2018-1523
DO - 10.21437/Interspeech.2018-1523
M3 - Conference article
AN - SCOPUS:85054955560
SN - 2308-457X
VL - 2018-September
SP - 436
EP - 440
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
T2 - 19th Annual Conference of the International Speech Communication, INTERSPEECH 2018
Y2 - 2 September 2018 through 6 September 2018
ER -