Automatic assessment of individual culture attribute of power distance using a social context-enhanced prosodic network representation

Fu Sheng Tsai, Hao Chun Yang, Wei Wen Chang, Chi Chun Lee

Research output: Contribution to journalConference article

Abstract

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.

Original languageEnglish
Pages (from-to)436-440
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2018-September
DOIs
Publication statusPublished - 2018 Jan 1
Event19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India
Duration: 2018 Sep 22018 Sep 6

Fingerprint

Speech communication
Network architecture
Visualization
Attribute
Neural networks
Experiments
Social Norms
Social Structure
Social Behavior
Binary Classification
Computational Analysis
Social Interaction
Network Architecture
High Power
Person
Enhancement
Context
Culture
Social Context
Neural Networks

Keywords

  • Behavioral signal processing
  • Center-loss embedding
  • Culture attribute
  • Power distance
  • Prosody

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Cite this

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abstract = "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.",
keywords = "Behavioral signal processing, Center-loss embedding, Culture attribute, Power distance, Prosody",
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AU - Chang, Wei Wen

AU - Lee, Chi Chun

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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.

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