The INTERSPEECH 2020 Computational Paralinguistics Challenge: Elderly Emotion, Breathing & Masks

By Björn Schuller, Anton Batliner, C. Bergler, Eva-Maria Messner, Antonia Hamilton, Shahin Amiriparian, Alice Baird, Georgios Rizos, Maximilian Schmitt, Lukas Stappen and 3 more. 2020

134 citations recorded by the source corpus.

The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the ‘usual’ C OM P AR E and BoAW features as well as deep unsupervised representation learning using the AU D EEP toolkit, and deep feature extraction from pre-trained CNNs using the D EEP S PECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Evaluation & Benchmarks, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jinyu Li
2. Yu Zhang
3. Tara N. Sainath
4. Yonghui Wu
5. Quoc V. Le
6. Chung-Cheng Chiu
7. Bo Li
8. Dong Yu

In Experimental and Cognitive Psychology

1. Wei Han
2. William Chan
3. Joel Shor
4. Aren Jansen

In Evaluation & Benchmarks

1. Yu Zhang
2. Wei Han
3. Chung-Cheng Chiu
4. Yonghui Wu
5. Bo Li
6. William Chan
7. Jiahui Yu
8. Bhuvana Ramabhadran

In Speech & Audio

1. Yu Zhang
2. Jinyu Li
3. Tara N. Sainath
4. Yonghui Wu
5. Chung-Cheng Chiu
6. Ruoming Pang
7. Bo Li
8. Wei Han

Citations in the corpus

• BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021, methodology): “‡The masked speech task performance is reported using unweighted average recall [90] instead of accuracy.”
• FRILL: A Non-Semantic Speech Embedding for Mobile Devices (2020, unknown): “Finally, in addition to the NOSS benchmark, we assess the quality of our embeddings on two privacy-sensitive, healthsensing tasks: human sounds classification [20] and face-mask speech detection [21].”
• Paralinguistic Classification of Mask Wearing by Image Classifiers and Fusion (2020, methodology): “Such feature sets were also used to set up the baseline for the current challenge [11].”