A Vocal Model to Predict Readiness under Sleep Deprivation

A variety of factors can affect cognitive readiness and influence human performance in tasks that are mission critical. Sleep deprivation is one of the most prevalent factors that degrade performance. One risk mitigation approach is to use vocal biomarkers to detect cognitive fatigue and resulting performance decrements [1], [2]. In this study, a group of 20 subjects were deprived of sleep for a period of 24 hours. Every two hours, they performed a battery of both speech tasks and cognitive performance tasks, including the psychomotor vigilance test (PVT). Performance on the PVT declined dramatically during nighttime hours between 2 AM and 8 AM. We demonstrate that a model using vocal biomarkers from read speech and free speech can be successfully trained to detect performance decrements on the PVT. We also demonstrate that the vocal model successfully generalizes to other outcomes at a similar level as PVT, detecting sleep deprivation (AUC=0.79) and cognitive performance declines on a battery of cognitive tasks (AUC=0.79). In comparison, using PVT as the basis for detecting sleep deprivation and performance declines resulted in AUC=0.75 and AUC=0.80, respectively.Clinical Relevance—This paper provides evidence to suggest that vocal models trained to predict changes in reaction times due to sleep deprivation can generalize to detect sleep deprivation and associated performance declines on more complex cognitive tasks