Multilingual Deception Detection by Autonomous Agents

Evgeny Hershkovitch Neiterman, Moshe Bitan, Amos Azaria

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

In this work we present the development of a multilingual deception detection model based on speech. In addition, we also develop a model that detects whether a statement will be perceived as a lie or not by human subjects. To this end, we developed a game for collecting a large scale and high quality labeled data-set in a controlled environments in English and Hebrew. We developed a model that can detect deception based only on a vocal statement from the participants of the experiment. The data-set will be released to the community.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Pages480-484
Number of pages5
ISBN (Electronic)9781450370240
DOIs
StatePublished - 20 Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period20/04/2024/04/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Funding

This research was supported in part by the Ministry of Science, Technology & Space, Israel and the Ministry of Science and Technology of Taiwan.

FundersFunder number
Ministry of Science, Technology & Space, Israel
Ministry of Science and Technology, Taiwan

    Keywords

    • Agents
    • Deception detection
    • Lie detection
    • Voice

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