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

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.

AI Hears Your Health: Computer Audition for Health Monitoring

Acoustic sounds produced by the human body reflect changes in our mental, physiological, and pathological states. A deep analysis of such audio that are of complex nature can give insight about imminent or existing health issues. For automatic processing and understanding of such data, sophisticated machine learning approaches are needed that can extract or learn robust features. In this paper, we introduce a set of machine learning toolkits both for supervised feature extraction and unsupervised representation learning from audio health data. We analyse the application of deep neural networks (DNNs), including end-to-end learning, recurrent autoencoders, and transfer learning for speech and body-acoustics health monitoring and provide state-of-the-art results for each area. As show-case examples, we pick three well-benchmarked examples for body-acoustics and speech, each, from the popular annual Interspeech Computational Paralinguistics Challenge (ComParE). In particular, the speech-based health tasks are COVID-19 speech analysis, recognition of upper respiratory tract infections, and continuous sleepiness recognition. The body-acoustics health tasks are COVID-19 cough analysis, speech breath monitoring, heartbeat abnormality recognition, and snore sound classification. The results for all tasks demonstrate the suitability of deep computer audition approaches for health monitoring and automatic audio-based early diagnosis of health issues. © 2021, Springer Nature Switzerland AG.