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On-Body Monitoring of Voice-Based Cognitive Load Features in an Auditory Working Memory Task

The ability to monitor an individual's cognitive load in operational and naturalistic settings is of great importance in improving military performance and readiness. This paper describes preliminary results of using an on-body, multimodal voice monitoring system for assessing cognitive load based on vocal characteristics extracted from noise-robust sensors. The main components of the system include a commercial wired electroglottograph (EGG) and a lightweight, flex circuit that houses two sensors: an acoustic MEMS microphone (MIC) and a non-acoustic neck-surface accelerometer (ACC). We conducted human subject experiments using a cognitive load protocol under quiet laboratory conditions and computed a previously investigated vocal biomarker (creaky voice quality) from the MIC, ACC, and EGG signals. Results demonstrated the potential of discriminating low versus high cognitive load using the creaky voice correlation structure within each of the three sensor domains. Combining MIC, ACC, and EGG hardware into an integrated system would provide complementary information for quantifying voice and speech biomarkers with a high degree of robustness to competing noise sources.