Multilingual Deception Detection by Autonomous Agents

Evgeny Hershkovitch Neiterman, Moshe Bitan, Amos Azaria

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

4 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
Pages480-484
Number of pages5
ISBN (Electronic)9781450370240
DOIs
StatePublished - 20 Apr 2020
Externally publishedYes
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

Keywords

  • Agents
  • Deception detection
  • Lie detection
  • Voice

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