Teachers and singers have been extensively studied and are shown to have a greater tendency to voice disorders. This study aimed to investigate the correlation between subjective and objective voice analysis pre- and post-shift among teleoperators in a tertiary hospital.

This was a prospective cohort study. Each patient underwent pre- and post-shift voice analysis.

Among 42 teleoperators, 28 patients (66.7 per cent) completed all the tests. Female predominance (62 per cent) was noted, with a mean age of 40 years. Voice changes during working were reported by 48.1 per cent. Pre- and post-shift maximum phonation time (p < 0.018) and Voice Handicap Index-10 (p < 0.011) showed significant results with no correlation noted between subjective and objective assessment.

Maximum phonation time and Voice Handicap Index-10 are good voice assessment tools. The quality of evidence is inadequate to recommend ‘gold standard’ voice assessment until a better-quality study has been completed.

Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).

N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally.

Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82).

Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.