The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation&Primates

Schuller, Bjorn W.; Batliner, Anton; Bergler, Christian; Mascolo, Cecilia; Han, Jing; Lefter, Iulia; Kaya, Heysem; Amiriparian, Shahin; Baird, Alice; Stappen, Lukas; Ottl, Sandra; Gerczuk, Maurice; Tzirakis, Panagiotis; Brown, Chloe; Chauhan, Jagmohan; Grammenos, Andreas; Hasthanasombat, Apinan; Spathis, Dimitris; Xia, Tong; Cicuta, Pietro; Rothkrantz, Leon J. M.; Zwerts, Joeri; Treep, Jelle; Kaandorp, Casper

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 SubChallenge, 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.