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

arXiv: https://arxiv.org/abs/2102.13468 · HTML: https://mlsanity.com/paper/2102.13468/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data](https://mlsanity.com/paper/2102.05225/) (2021)
- [auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1712.04382/) (2017)
- [openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit](https://mlsanity.com/paper/1605.06778/) (2016)
- [End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning](https://mlsanity.com/paper/1802.01115/) (2018)
- [Detection of COVID-19 through the analysis of vocal fold oscillations](https://mlsanity.com/paper/2010.10707/) (2020)
- [Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification](https://mlsanity.com/paper/2101.10390/) (2021)

## Built on by (most cited first, 30 of 32)

- [Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition](https://mlsanity.com/paper/2205.03433/) (2022)
- [The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress](https://mlsanity.com/paper/2104.07123/) (2021)
- [The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress](https://mlsanity.com/paper/2207.05691/) (2022)
- [SERAB: A multi-lingual benchmark for speech emotion recognition](https://mlsanity.com/paper/2110.03414/) (2021)
- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data](https://mlsanity.com/paper/2104.11629/) (2021)
- [The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation](https://mlsanity.com/paper/2305.03369/) (2023)
- [COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features](https://mlsanity.com/paper/2104.02477/) (2021)
- [The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests](https://mlsanity.com/paper/2304.14882/) (2023)
- [Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data](https://mlsanity.com/paper/2104.02005/) (2021)
- [Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges](https://mlsanity.com/paper/2107.14549/) (2021)
- [The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes](https://mlsanity.com/paper/2205.06799/) (2022)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features](https://mlsanity.com/paper/2206.05018/) (2022)
- [Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts](https://mlsanity.com/paper/2206.12469/) (2022)
- [Interpretability Analysis of Deep Models for COVID-19 Detection](https://mlsanity.com/paper/2211.14372/) (2022)
- [The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition](https://mlsanity.com/paper/2406.07753/) (2024)
- [TinyM2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices](https://mlsanity.com/paper/2202.04303/) (2022)
- [A Summary of the ComParE COVID-19 Challenges](https://mlsanity.com/paper/2202.08981/) (2022)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model](https://mlsanity.com/paper/2406.18731/) (2024)
- [COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection](https://mlsanity.com/paper/2206.11045/) (2022)
- [Visual Transformers for Primates Classification and Covid Detection](https://mlsanity.com/paper/2212.10093/) (2022)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- [Privacy Constrained Fairness Estimation for Decision Trees](https://mlsanity.com/paper/2312.08413/) (2023)
- [Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data](https://mlsanity.com/paper/2402.07619/) (2024)
- [Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet](https://mlsanity.com/paper/2203.06064/) (2022)
- [Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 Features](https://mlsanity.com/paper/2203.15183/) (2022)
- [A Temporal-oriented Broadcast ResNet for COVID-19 Detection](https://mlsanity.com/paper/2203.17012/) (2022)
- [Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data](https://mlsanity.com/paper/2209.03727/) (2022)

## Abstract (arXiv)

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.