loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Katashi Nagao 1 ; Kosuke Okamoto 1 ; Shimeng Peng 1 and Shigeki Ohira 2

Affiliations: 1 Graduate School of Informatics, Nagoya University, Nagoya and Japan ; 2 Information Technology Center, Nagoya University, Nagoya and Japan

Keyword(s): Evaluation of Discussion Skills, Discussion Mining, Machine Learning, Learning Analytics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: In this paper, we propose a system for improving the discussion skills of participants in a meeting by automatically evaluating statements in the meeting and effectively feeding back the results of the evaluation to them. To evaluate the skills automatically, the system uses both acoustic features and linguistic features of statements. It evaluates the way a person speaks, such as their “voice size,” on the basis of the acoustic features, and it also evaluates the contents of a statement, such as the “consistency of context,” on the basis of linguistic features. These features can be obtained from meeting minutes. Since it is difficult to evaluate the semantic contents of statements such as the “consistency of context,” we build a machine learning model that uses the features of minutes such as speaker attributes and the relationship of statements. In addition, we argue that participants’ heart rate (HR) data can be used to effectively evaluate their cognitive performance, specifical ly the performance in a discussion that consists of several Q&A segments (question-and-answer pairs). We collect HR data during a discussion in real time and generate machine-learning models for evaluation. We confirmed that the proposed method is effective for evaluating the discussion skills of meeting participants. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.236.51.151

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Nagao, K.; Okamoto, K.; Peng, S. and Ohira, S. (2019). Discussion-skill Analytics with Acoustic, Linguistic and Psychophysiological Data. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, ISBN 978-989-758-382-7; ISSN 2184-3228, pages 409-418. DOI: 10.5220/0008332304090418

@conference{kdir19,
author={Katashi Nagao. and Kosuke Okamoto. and Shimeng Peng. and Shigeki Ohira.},
title={Discussion-skill Analytics with Acoustic, Linguistic and Psychophysiological Data},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,},
year={2019},
pages={409-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008332304090418},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,
TI - Discussion-skill Analytics with Acoustic, Linguistic and Psychophysiological Data
SN - 978-989-758-382-7
IS - 2184-3228
AU - Nagao, K.
AU - Okamoto, K.
AU - Peng, S.
AU - Ohira, S.
PY - 2019
SP - 409
EP - 418
DO - 10.5220/0008332304090418

0123movie.net