Automatic detection of sleepiness-related symptoms and syndromes using voice and speech biomarkers

Sleepiness is a major public and personal health issue. Measuring sleepiness in patients’ everyday living conditions would represent a significant advancement in managing them. Voice is a signal linked to numerous health dimensions, including sleepiness. However, previous research has focused on short-term subjective sleepiness, using corpora with questionable medical validity and without measuring the specificity of identified voice biomarkers. In this article, we estimate different symptoms and long-term sleepiness-related syndromes in hypersomnolent patients. To achieve this, we have developed machine learning models that identify biomarkers that are sensitive and specific to sleepiness, reaching classification performances (Unweighted Average Recall) above 75%. Importantly, we only used statistical functions (decorrelation, Principal Component Analysis, Sensitivity test, linear classifier) so that this model remains simple and explainable to collaborating clinicians. We then leverage this explainability to identify specific vocal and speech manifestations for each type of sleepiness. By combining objective measures and the analysis of vocal characteristics, our approach provides a comprehensive understanding of long-term sleepiness and enhances patient care and management. This research holds great potential for advancing the field of digital health and contributing to improved well-being for individuals affected by sleepiness-related conditions.