Vocal Instability as a Sensitive Biomarker for Driving Stress: Decoupling Cognitive Load and Environmental Friction in a Real-World Dual-Task Protocol

Driver stress and cognitive workload are regarded as critical safety determinants within modern transportation systems. Although vocal acoustic analysis is considered a promising, non-invasive monitoring technique, the existing literature has been observed to generally lack ecological validity, and difficulties have been experienced in causally attributing the source of stress—whether it is internal cognitive load (CL) from secondary tasks or external environmental friction (EF) from traffic.To address this gap, a single-subject (N=1) case study design was utilized within a real-world dual-task protocol. Within this protocol, a driver was required to maintain continuous conversation while navigating two distinct environments: a high-friction urban congestion segment (short route) and a hybrid urban and intercity segment (long route). A custom weighted acoustic stress index and the instantaneous pitch standard deviation (vocal instability) were analyzed.The findings demonstrate that the constant demand of the dual-task establishes a dominant, consistent moderate stress baseline (approx. 34–36%) that is decoupled from routine traffic fluctuations and congestion level differences. Although the average stress level was maintained consistently, pitch standard deviation was proven to be a more sensitive metric: this metric was found to be significantly lower on the long route (hybrid segment) when compared to the short route (pure urban congestion). With this finding, the ability of vocal instability to effectively decouple the contributions of CL and EF is confirmed; thus, empirical evidence is provided that a stabilizing effect on the voice is created by low-demand highway segments, even if the overall vocal load remains moderate. Through this research, vocal instability is validated as a valuable, sensitive biomarker that is necessary for the development of context-aware in-vehicle systems capable of accurately distinguishing between distraction-related stress and environmental stress.