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This paper is in the following e-collection/theme issue:

Artificial Intelligence (4526) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (3067) New Methods (1731) Depression and Mood Disorders; Suicide Prevention (2389) Machine Learning (3036) Applications of AI (844) Voice Phenotyping and Vocal Biomarkers (32)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67772, first published .
Woman wearing headset, working on computer with bright screens in dark room

Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach

Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach

Authors of this article:

Zhengyuan Su1, 2, 3 Author Orcid Image ;   Huadong Jiang4 Author Orcid Image ;   Ying Yang1, 2 Author Orcid Image ;   Xiangqing Hou1, 2, 3 Author Orcid Image ;   Yanli Su4 Author Orcid Image ;   Li Yang1, 2 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Rodrigo I, Duñabeitia J. Listening to the Mind: Integrating Vocal Biomarkers into Digital Health. Brain Sciences 2025;15(7):762 View
  2. Hu Y, Wu R, Su M, Lin I, Shen C. Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application. JMIR Medical Informatics 2025;13:e66907 View
  3. Lyu M, Tan L, Liu F, Lei J, Jiang T, Qi J, Wang X, Yang H, Zhong J, Feng Z. Multiverse analysis of machine learning: classification between groups defined by suicidal ideation screening status using acoustic features in college students. Frontiers in Psychology 2026;17 View

Conference Proceedings

  1. Sun Q, Zhang S, Gao Y, Li Y, Li J, Li X. 2025 IEEE International Conference on Data Mining Workshops (ICDMW). Speech Feature Extraction and Selection for Mental Health Analysis: Methods, Tools, and Challenges View