JAIST Repository >
School of Information Science >
Grants-in-aid for Scientific Research Papers >
FY 2017 >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15393

Title: マルチモーダル会話モデリングに基づくグループ意思決定プロセスの解析
Other Titles: Analyzing group decision making process based on multimodal conversation modeling
Authors: 岡田, 将吾
Authors(alternative): Okada, Shogo
Keywords: マルチモーダルインタラクション
Issue Date: 11-Jun-2018
Abstract: 本研究の目的は,意思決定を行うためのグループ会議に焦点を当て,会議中に交わされる会話者の言語・非言語情報から,意思決定が行われるまでのプロセスを会話データより客観的に解析出来る技術を新規に開発することである. 言語・非言語情報(発話,音声,動作)と,アノテーションされたグループディスカッションの質を紐づけることで,良質なディスカッションに特有に現れる非言語情報や,コミュニケーション能力の高い人に見られる特有の言語パターンを抽出出来るフレームワークを構築した.機械学習のSupport Vector Machine を用いた結果,最大82%の精度で推定するモデルを構築した.:This research project focuses on group discussion for a problem solving and develops a framework for analyzing the group decision making process based on verbal and nonverbal (multimodal) information which observed from group members. We defined the quality of group output as an index set of social science (“product dimension”), which proposed by Hackman. The annotation data of the quality of group output has been collected. The machine learning model it developed to predict the product dimension from multimodal information including dialog transcription, head motion, speech prosody and turn taking. Novel co-occurrence data mining is proposed to capture the group interaction and multimodal patterns. Through the machine learning modeling and data mining, the specific multimodal features observed in group discussion process with high/low quality can be discovered automatically. Best prediction accuracy of product dimension is 82 % in binary classification task (high or low of quality).
Description: 基盤研究(C)(一般)
Language: jpn
URI: http://hdl.handle.net/10119/15393
Appears in Collections:2017年度 (FY 2017)

Files in This Item:

File Description SizeFormat
15K00300seika.pdf348KbAdobe PDFView/Open

All items in DSpace are protected by copyright, with all rights reserved.


Contact : Library Information Section, Japan Advanced Institute of Science and Technology