Sleep deprivation measured by voice analysis

Abstract

AbstractWe show that sleep deprivation in otherwise normal and healthy adults can be detected through machine-learning analysis of vocal recordings. Importantly, we used fully generic acoustic features, derived from auditory models, together with our own machine learning interpretation method, derived from neuroscience. Sleep deprivation impacted two broad types of acoustic features: one related to speech rhythms, the other related to the timbre of the voice. Such features plausibly reflect two independent physiological processes: one explicit, the cognitive control of speech production, and the other implicit, the inflammation of the vocal apparatus. Crucially, the relative balance of the two processes varied widely across individuals, consistent with the known but unexplained variability in responses to sleep deprivation. Overall, our results suggest that the voice may be used as a “sleep stethoscope” to characterize the individual effects of sleep deprivation. Moreover, the method we applied is fully general and could be adapted to any future investigation of vocal biomarkers using machine-learning techniques.Author summarySleep deprivation has an ever-increasing impact on individuals and societies, from accidents to chronic conditions costing billions to health systems. Yet, to date, there is no quick and objective test for sleep deprivation. We show that sleep deprivation can be detected at the individual level with voice recordings, outlining future cost-effective and non-invasive “sleep stethoscopes”. Importantly, we focused on interpretability, which identified two independent physiological effects of sleep deprivation: a change in prosody, related to cognitive control, and a change in timbre, related to inflammation. This also revealed a striking variability in individual reactions to the same deprivation. The neuroscientific framework we developed, combining auditory models and machine learning, is freely available and could be adapted to any vocal biomarker.