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  1. 会議発表論文
  2. EDO 2017

Suppressed Negative-Emotion-Detecting Method by using Transitions in Facial Expressions and Acoustic Features

https://hiroshima-cu.repo.nii.ac.jp/records/1630
https://hiroshima-cu.repo.nii.ac.jp/records/1630
4901f528-8c7e-4e0b-bc90-9c358ea269a0
名前 / ファイル ライセンス アクション
EDO2017_6.pdf EDO2017_6.pdf (2.1 MB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2023-03-02
タイトル
タイトル Suppressed Negative-Emotion-Detecting Method by using Transitions in Facial Expressions and Acoustic Features
言語
言語 eng
キーワード
主題Scheme Other
主題 emotion recognition
キーワード
主題Scheme Other
主題 transition of facial expression
キーワード
主題Scheme Other
主題 micro expression
キーワード
主題Scheme Other
主題 acoustic feature
キーワード
主題Scheme Other
主題 suppressed emotion
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者 UEMURA, Joji

× UEMURA, Joji

UEMURA, Joji

ja-Kana ウエムラ, ジョウジ

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MERA, Kazuya

× MERA, Kazuya

MERA, Kazuya

ja-Kana メラ, カズヤ

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KUROSAWA, Yoshiaki

× KUROSAWA, Yoshiaki

KUROSAWA, Yoshiaki

ja-Kana クロサワ, ヨシアキ

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TAKEZAWA, Toshiyuki

× TAKEZAWA, Toshiyuki

TAKEZAWA, Toshiyuki

ja-Kana タケザワ, トシユキ

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上村, 譲史

× 上村, 譲史

en 上村, 譲史

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目良, 和也

× 目良, 和也

en 目良, 和也

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黒澤, 義明

× 黒澤, 義明

en 黒澤, 義明

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竹澤, 寿幸

× 竹澤, 寿幸

en 竹澤, 寿幸

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抄録
内容記述タイプ Abstract
内容記述 We propose a method of detecting suppressed/concealed negative emotion during compliment utterance. When people suppress/conceal emotions, very brief facial expressions called “micro expression” often appear. In order to detect such short-duration facial expression, we propose 90 features calculated from the contours of likelihood ratios for each of the five emotions (happiness, sadness, surprise, anger, and neutral). Likelihood ratios are calculated from still images in a video every 100 milliseconds. Furthermore, 384 acoustic features are calculated for multimodal analysis. Three machine learning classifiers by Support Vector Machines were constructed by using feature sets consist of facial-expression-transition, voice, and both of them, and the classifiers were evaluated how they can detect insincere compliments in Japanese. The experimental results indicate that the feature set including both of facial-expression-transition and voice was the most superior. Its precision and recall of insincerity detection and the total accuracy rate were 0.50, 0.44, and 0.64, respectively. The results were better than the annotation results by non-expert participants.
内容記述
内容記述タイプ Other
内容記述 The Second Workshop on Processing Emotions, Decisions and Opinions (EDO 2017) The 8th Language and Technology Conference (LTC) 2017/11/17, Poznań, Poland|最優秀論文賞(Best Paper Award)受賞論文, 査読有
書誌情報 The Second Workshop on Processing Emotions, Decisions and Opinions (EDO 2017)

p. 122-127, 発行日 2017-11-17
出版者
出版者 The 8th Language and Technology Conference (LTC)
フォーマット
内容記述タイプ Other
内容記述 application/pdf
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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