The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
Year: 2021
Type: preprint
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 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.
Source: arXiv (Cornell University)
Authors: Schuller, Björn 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, Chloë, Chauhan, Jagmohan, Grammenos, Andreas, Hasthanasombat, Apinan, Spathis, Dimitris, Xia, Tong, Cicuta, Pietro, Rothkrantz, Leon J. M., Zwerts, Joeri, Treep, Jelle, Kaandorp, Casper
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3134945014