TY - GEN
T1 - Multilingual Deception Detection by Autonomous Agents
AU - Hershkovitch Neiterman, Evgeny
AU - Bitan, Moshe
AU - Azaria, Amos
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - 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.
AB - 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.
KW - Agents
KW - Deception detection
KW - Lie detection
KW - Voice
UR - https://www.scopus.com/pages/publications/85091702215
U2 - 10.1145/3366424.3384369
DO - 10.1145/3366424.3384369
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AN - SCOPUS:85091702215
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 480
EP - 484
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -