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Machine Learning Approach to Identifying Empathy Using the Vocals of Mental Health Helpline Counselors: Algorithm Development and Validation

Machine Learning Approach to Identifying Empathy Using the Vocals of Mental Health Helpline Counselors: Algorithm Development and Validation

The level of suicide risk of each caller had been previously assessed by counselors using the Columbia Suicide Severity Rating Scale (C-SSRS) to differentiate between calls featuring high suicide risk (with C-SSRS ratings of 6‐7) and calls with low risk of suicide (with C-SSRS ratings of 1‐2; please refer to Iyer et al [17] for further details [18]). Only the counselors’ voice recordings were used in this study.

Ruvini Sanjeewa, Ravi Iyer, Pragalathan Apputhurai, Nilmini Wickramasinghe, Denny Meyer

JMIR Form Res 2025;9:e67835

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Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis

Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis

In a review by Iyer and Meyer [18], timing patterns of speech were able to detect high risk of suicide callers compared with their comparison group with a median accuracy of 95%. Other vocal characteristics such as power spectral density sub-bands and mel-frequency cepstral coefficients demonstrated at least 80% accuracy in differentiating groups.

Kathleen de Boer, Jessica L Mackelprang, Maja Nedeljkovic, Denny Meyer, Ravi Iyer

JMIR Res Protoc 2024;13:e54966

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