The INTERSPEECH 2016 computational paralinguistics challenge: Deception, sincerity & native language

Björn Schuller; Stefan Steidl; Anton Batliner; Julia Hirschberg; Judee K. Burgoon; Alice Baird; Aaron Elkins; Yue Zhang; Eduardo Coutinho; Keelan Evanini

doi:10.21437/Interspeech.2016-129

The INTERSPEECH 2016 computational paralinguistics challenge: Deception, sincerity & native language

Björn Schuller
, Stefan Steidl
, Anton Batliner
, Julia Hirschberg
, Judee K. Burgoon
, Alice Baird
, Aaron Elkins
, Yue Zhang
, Eduardo Coutinho
, Keelan Evanini

School of Human Ecology-RES

Research output: Contribution to journal › Conference article › peer-review

320 Scopus citations

Abstract

The INTERSPEECH 2016 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: classification of deceptive vs. non-deceptive speech, the estimation of the degree of sincerity, and the identification of the native language out of eleven L1 classes of English L2 speakers. In this paper, we describe these sub-challenges, their conditions, the baseline feature extraction and classifiers, and the resulting baselines, as provided to the participants.

Original language	English (US)
Pages (from-to)	2001-2005
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume	08-12-September-2016
DOIs	https://doi.org/10.21437/Interspeech.2016-129
State	Published - 2016
Event	17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States Duration: Sep 8 2016 → Sep 16 2016

Keywords

Challenge
Computational paralinguistics
Deception
Native language identification
Sincerity

ASJC Scopus subject areas

Software
Signal Processing
Language and Linguistics
Modeling and Simulation
Human-Computer Interaction

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10.21437/Interspeech.2016-129

Cite this

Schuller, B., Steidl, S., Batliner, A., Hirschberg, J., Burgoon, J. K., Baird, A., Elkins, A., Zhang, Y., Coutinho, E., & Evanini, K. (2016). The INTERSPEECH 2016 computational paralinguistics challenge: Deception, sincerity & native language. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 08-12-September-2016, 2001-2005. https://doi.org/10.21437/Interspeech.2016-129

The INTERSPEECH 2016 computational paralinguistics challenge: Deception, sincerity & native language. / Schuller, Björn; Steidl, Stefan; Batliner, Anton et al.
In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Vol. 08-12-September-2016, 2016, p. 2001-2005.

Research output: Contribution to journal › Conference article › peer-review

Schuller, B, Steidl, S, Batliner, A, Hirschberg, J, Burgoon, JK, Baird, A, Elkins, A, Zhang, Y, Coutinho, E & Evanini, K 2016, 'The INTERSPEECH 2016 computational paralinguistics challenge: Deception, sincerity & native language', Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 08-12-September-2016, pp. 2001-2005. https://doi.org/10.21437/Interspeech.2016-129

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abstract = "The INTERSPEECH 2016 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: classification of deceptive vs. non-deceptive speech, the estimation of the degree of sincerity, and the identification of the native language out of eleven L1 classes of English L2 speakers. In this paper, we describe these sub-challenges, their conditions, the baseline feature extraction and classifiers, and the resulting baselines, as provided to the participants.",

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AU - Hirschberg, Julia

AU - Burgoon, Judee K.

AU - Baird, Alice

AU - Elkins, Aaron

AU - Zhang, Yue

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KW - Challenge

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KW - Deception

KW - Native language identification

KW - Sincerity

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The INTERSPEECH 2016 computational paralinguistics challenge: Deception, sincerity & native language

Abstract

Keywords

ASJC Scopus subject areas

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