The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
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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.
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Zero-Shot Audio Classification Using Synthesised Classifiers and Pre-Trained Models
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- Authors
- Schuller, B. W., Batliner, A., Bergler, C., Mascolo, C., Han, J., Lefter, I., Kaya, H., Amiriparian, S., Baird, A., Stappen, L., Ottl, S., Gerczuk, M., Tzirakis, P., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., Rothkrantz, L. J. M., Zwerts, J. A., Treep, J., & Kaandorp, C. S.
- Affiliations
- University of Augsburg (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University of Cambridge (GB)
- Delft University of Technology (NL)
- Utrecht University (NL)
- Imperial College London (GB)
- Corresponding author
- Schuller, Björn W.
- ORCID
- Schuller, Björn W.
- Batliner, Anton
- Bergler, Christian
- 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
- Cicuta, Pietro
- Zwerts, Joeri A.
- Treep, Jelle
- Kaandorp, Casper S.
- Venue
- Interspeech 2021
- Publisher
- ISCA
- Published
- 2021-08-30
- Pages
- 431-435
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W3196831814
- Cited by
- 89
- References
- 33
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 12 · 2023: 16 · 2022: 32 · 2021: 18
- FWCI
- 9.01
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Interspeech (USB) (publishedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (442218748)
- H2020 European Research Council (833296)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- computational paralinguistics, COVID-19 detection, cough sounds, speech analysis, dialogue analysis, pre-trained CNN features
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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