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Estimating Personality Impression from Speed Record Using Hidden Markov Models
Author(s) -
JIN YICHENG,
SAKUMA TAKUTO,
KATO SHOHEI,
KUNITACHI TSUTOMU
Publication year - 2016
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11900
Subject(s) - conscientiousness , hidden markov model , extraversion and introversion , big five personality traits , personality , openness to experience , computer science , impression formation , impression , speech recognition , subconscious , psychology , artificial intelligence , cognitive psychology , social psychology , perception , social perception , medicine , world wide web , alternative medicine , pathology , neuroscience
SUMMARY When people listen to other's speech for the first time, they always attribute personality traits to the speaker subconsciously. We consider that if robots can predict personality traits of users from their speech, the communication in human–machine interaction will improve significantly. This paper proposes an approach for the automatic estimation of the traits, in which the listeners attribute to unanimous speakers. And the discrimination experiments based on hidden Markov model and canonical discrimination analysis show that, it is possible to predict with high accuracy (more than 75%), whether a speaker is perceived to be in the higher or lower part of the “Extraversion”, “Openness”, and “conscientiousness” by using nonverbal information.