e.g. mhealth
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Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications
Database queries were performed using the Pub Med, IEEE Xplore, ar Xiv, and Science Direct databases up until February 2025, with the following keyword search: (“emotion recognition” OR “affective computing” OR “emotional analysis”) AND (“psychiatry” OR “psychology”) AND (“speech” OR “voice”).
During the screening process, 2 authors applied the eligibility criteria and selected the studies to be included in the systematic review.
JMIR Ment Health 2025;12:e74260
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Collection and Analysis of Repeated Speech Samples: Methodological Framework and Example Protocol
Potential confounding factors related to the speaker include hormonal variations within the menstrual cycle [12,13], fatigue [14], voice use habits [15-17], emotion [18], and hydration [19]. Systematic changes with age, menopause, and medication use have also been reported [20-23].
JMIR Res Protoc 2025;14:e69431
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Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study
Clinicians are trained to recognize variations in language and voice, along with gestures and facial expressions, implicitly assessing both coherence and organization of speech and natural language features. However, this process is inevitably vulnerable to inconsistencies and biases.
JMIR Form Res 2025;9:e65555
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach
However, voice messages become particularly important in special contexts such as in crisis hotline calls [24].
As a suicide prevention method, crisis hotlines play a crucial role in early detection and response to suicide risk [25]. The World Health Organization estimates that there are more than 1000 crisis hotlines worldwide.
J Med Internet Res 2025;27:e67772
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Whose voice?’ Co-creating a technology research roadmap with autism stakeholdersvoice
JMIR Rehabil Assist Technol 2025;12:e63235
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One of the drawbacks of these studies is that, apart from a single study [15], voice recordings were collected at a single time point within a menstrual phase; thus, day-to-day changes within the voice were notably not assessed. Ovulation itself is a result of longitudinal changes in hormone values compared to the previous days. Therefore, it is essential to trace day-to-day changes of acoustic characteristics to understand the effect of ovulation on voice signals.
JMIR Form Res 2025;9:e65448
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While the broad ramifications of obesity are well documented, recent scientific inquiries have begun to elucidate the potential alterations in voice characteristics that may be concurrent with obesity [5,6]. Several mechanisms are postulated to explain these alterations in vocal attributes. The deposition of adipose tissue near the vocal folds and larynx may influence vocal resonance and pitch, often resulting in variations in voice quality [7].
JMIR AI 2024;3:e54885
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These biomarkers are unique characteristics or acoustic patterns of an individual’s voice that can hold valuable information about their physical and mental well-being [2]. Human voice production requires the coordination of multiple biological systems; perturbations in these systems induced by various conditions or diseases can result in alterations in the characteristics of the human voice [3].
JMIR Biomed Eng 2024;9:e56246
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Deepfakes are generated through the aggregation of substantial data sets, including voice recordings, images, and video segments [3]. This research specifically targets the detection of audio deepfakes, relying solely on voice data for both deepfake development and detection method testing.
JMIR Biomed Eng 2024;9:e56245
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Smartwatch activity data [5-8] and voice [9-11] have been demonstrated to predict depression, anxiety, and stress with high levels of accuracy. The novelty of our study lies in three pillars:
First, our study is designed to collect longitudinal data from each participant. In contrast, previous studies about detecting depression from voice relied on data sets consisting of a single data point per participant for a one-size-fits-all model.
JMIR Res Protoc 2023;12:e48210
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