The INTERSPEECH 2021 computational paralinguistics challenge
COVID-19 cough, COVID-19 speech, escalation & primates
Björn W. Schuller (Universität Augsburg, Imperial College London)
Anton Batliner (Universität Augsburg, FAU University of Erlangen-Nuremberg, Erlangen)
Christian Bergler (FAU University of Erlangen-Nuremberg, Erlangen)
Cecilia Mascolo (University of Cambridge)
Jing Han (University of Cambridge)
Iulia Lefter (TU Delft - Technology, Policy and Management)
Heysem Kaya (Universiteit Utrecht)
Shahin Amiriparian (Universität Augsburg)
Leon J.M. Rothkrantz (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation Sub- Challenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. 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 add deep end-to-end sequential modelling, and partially linguistic analysis.