Automated Vocal Fatigue Screening in Professional Voice Users: Development and Occupational Validation of an Automated Assessment System.

INTRODUCTION: Professional voice users face an elevated risk of vocal injury from sustained occupational voice use, yet scalable tools for workplace surveillance remain limited. OBJECTIVE: To develop and validate an automated vocal fatigue monitoring system for occupational health applications. METHODS: We developed a speaker-independent assessment system using deep learning embeddings trained on diverse speech samples. Validation included a correlation with the Vocal Fatigue Index (VFI) in 27 participants (9 teachers, 18 general population) and temporal sensitivity assessment using twice-daily recordings from 8 teachers over 5 workdays. RESULTS: Automated scores demonstrated very strong correlation with VFI (r = 0.942, P < 0.001), increased significantly across work shifts (d = 1.00, P < 0.001), and correlated with speaking duration (r = 0.544, P = 0.0003). Teachers showed substantially higher fatigue than the general population (d = 3.21, P < 0.001). CONCLUSIONS: The system demonstrates promising early validation for automated shift-level screening and speaker-independent assessment of occupational vocal load for integration into workplace health surveillance and vocal injury prevention programs.