The Phonetic Footprint of Covid-19?

Against the background of the ongoing pandemic, this year's Computational Paralinguistics Challenge featured a classification problem to detect Covid-19 from speech recordings. The presented approach is based on a phonetic analysis of speech samples, thus it enabled us not only to discriminate between Covid and non-Covid samples, but also to better understand how the condition influenced an individual's speech signal. Our deep acoustic model was trained with datasets collected exclusively from healthy speakers. It served as a tool for segmentation and feature extraction on the samples from the challenge dataset. Distinct patterns were found in the embeddings of phonetic classes that have their place of articulation deep inside the vocal tract. We observed profound differences in classification results for development and test splits, similar to the baseline method. We concluded that, based on our phonetic findings, it was safe to assume that our classifier was able to reliably detect a pathological condition located in the respiratory tract. However, we found no evidence to claim that the system was able to discriminate between Covid-19 and other respiratory diseases.