Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis

Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82–86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity

Investigating word affect features and fusion of probabilistic predictions incorporating uncertainty in AVEC 2017

© 2017 Association for Computing Machinery. Predicting emotion intensity and severity of depression are both challenging and important problems within the broader field of affective computing. As part of the AVEC 2017, we developed a number of systems to accomplish these tasks. In particular, word affect features, which derive human affect ratings (e.g. arousal and valence) from transcripts, were investigated for predicting depression severity and liking, showing great promise. A simple system based on the word affect features achieved an RMSE of 6.02 on the test set, yielding a relative improvement of 13.6% over the baseline. For the emotion prediction sub-challenge, we investigated multimodal fusion, which incorporated a measure of uncertainty associated with each prediction within an Output-Associative fusion framework for arousal and valence prediction, whilst liking prediction systems mainly focused on text-based features. Our best emotion prediction systems provided significant relative improvements over the baseline on the test set of 39.5%, 17.6%, and 29.3% for arousal, valence, and liking. Of particular note is that consistent improvements were observed when incorporating prediction uncertainty across various system configurations for predicting arousal and valence, suggesting the importance of taking into consideration prediction uncertainty for fusion and more broadly the advantages of probabilistic predictions