Automatic Detection Of Sleepiness-Related Syndromes and Symptoms Using Voice and Speech Biomarkers

Abstract

This article is about the automatic estimation of sleepiness in hypersomnia patients recorded during a reading task. Based on the Multiple Sleep Latency Corpus, our main contribution is to explore new formulations of sleepiness detection in speech by specifying and performing five sleepiness-related classification tasks. We automatically classify three symptoms, and two syndromes, i.e. combinations of symptoms that are closer to clinical reasoning. Another contribution of this paper is the use of a simple and interpretable pipeline integrating selecting voice biomarkers of sleepiness, i.e. features that are both sensible and specific to sleepiness. In particular, specificity is adressed integrating a decorrelation step in the pipeline, which allows to certify that the descriptors selected by the pipeline are indeed specific of sleepiness with respect to 7 cofactors (age, BMI, etc.).