TY - GEN
T1 - The INTERSPEECH 2020 computational paralinguistics challenge
T2 - 21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
AU - Schuller, Björn W.
AU - Batliner, Anton
AU - Bergler, Christian
AU - Messner, Eva Maria
AU - Hamilton, Antonia
AU - Amiriparian, Shahin
AU - Baird, Alice
AU - Rizos, Georgios
AU - Schmitt, Maximilian
AU - Stappen, Lukas
AU - Baumeister, Harald
AU - MacIntyre, Alexis Deighton
AU - Hantke, Simone
N1 - Publisher Copyright:
Copyright © 2020 ISCA
PY - 2020
Y1 - 2020
N2 - The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.
AB - The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.
KW - Breathing
KW - Challenge
KW - Computational Paralinguistics
KW - Elderly Emotion
KW - Speech under Mask
UR - https://www.scopus.com/pages/publications/85096140329
U2 - 10.21437/Interspeech.2020-32
DO - 10.21437/Interspeech.2020-32
M3 - Conference contribution
AN - SCOPUS:85096140329
SN - 9781713820697
T3 - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
SP - 2042
EP - 2046
BT - Interspeech 2020
PB - International Speech Communication Association
Y2 - 25 October 2020 through 29 October 2020
ER -