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Acoustical properties of speech as indicators of depression and suicidal risk
Abstract
Acoustic properties of speech have previously been identified as possible cues to depression, and there is evidence that certain vocal parameters may be used further to objectively discriminate between depressed and suicidal speech. Studies were performed to analyze and compare the speech acoustics of separate male and female samples comprised of normal individuals and individuals carrying diagnoses of depression and high-risk, near-term suicidality. The female sample consisted of ten control subjects, 17 dysthymic patients, and 21 major depressed patients. The male sample contained 24 control subjects, 21 major depressed patients, and 22 high-risk suicidal patients. Acoustic analyses of voice fundamental frequency (Fo), amplitude modulation (AM), formants, and power distribution were performed on speech samples extracted from audio recordings collected from the sample members. Multivariate feature and discriminant analyses were performed on feature vectors representing the members of the control and disordered classes. Features derived from the formant and power spectral density measurements were found to be the best discriminators of class membership in both the male and female studies. AM features emerged as strong class discriminators of the male classes. Features describing Fo were generally ineffective discriminators in both studies. The results support theories that identify psychomotor disturbances as central elements in depression and suicidality.
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“Acoustic properties of speech in terms of formants and power distribution were found to be best discriminators to distinguish between depression and suicidality individuals and healthy individuals [59].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [59]
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Huang, K.-Y., Wu, C., Su, M.-H., & Kuo, Y.-T. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2803178 · Cited by 68
Ye, J., Wen, X.-C., Wang, X.-Z., Xu, Y. D., Luo, Y., Wu, C., Chen, L., & Liu, K. (2022) · Speech Communication · doi:10.1016/j.specom.2022.07.005 · Cited by 64
Zhao, Q., Fan, H., Li, Y., Liu, L., Wu, Y., Zhao, Y., Tian, Z.-X., Wang, Z., Tan, Y., & Tan, S. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.815678 · Cited by 63
Investigating the speech characteristics of suicidal adolescents
Scherer, S. J., Pestian, J. P., & Morency, L. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6637740 · Cited by 63
Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis
Sturim, D. E., Torres‐Carrasquillo, P. A., Quatieri, T. F., Malyska, N., & McCree, A. V. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-746 · Cited by 62
Prominence features: Effective emotional features for speech emotion recognition
Jing, S., Mao, X., & Chen, L. (2017) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.016 · Cited by 60
Comparing objective feature statistics of speech for classifying clinical depression
Moore, E., Clements, M. A., Peifer, J. W., & Weisser, L. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2004.1403079 · Cited by 60
Natural Language Processing as an Emerging Tool to Detect Late-Life Depression
DeSouza, D. D., Robin, J., Gumus, M., & Yeung, A. T. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.719125 · Cited by 59
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion RecognitionPreprint
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Jahangir, R., Teh, Y. W., Mujtaba, G., Alroobaea, R., Shaikh, Z. H., & Ali, I. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01294-x · Cited by 56
Metadata
- Authors
- France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., & Wilkes, M.
- Affiliations
- L-3 Communications (United States) (US)
- Vanderbilt University (US)
- Norwalk Hospital (US)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2000-07
- Volume
- 47
- Issue
- 7
- Pages
- 829-837
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2148822124
- Cited by
- 504
- References
- 41
- Citations by year
- 2026: 16 · 2025: 30 · 2024: 31 · 2023: 37 · 2022: 34 · 2021: 45 · 2020: 41 · 2019: 25 · 2018: 34 · 2017: 27 · 2016: 26 · 2015: 27 · 2014: 23 · 2013: 25 · 2012: 17
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- speech acoustics, depression, suicide risk, voice fundamental frequency, amplitude modulation, formants, power spectral density, dysthymia, major depressive disorder, sex differences, psychomotor disorders
- MeSH terms
- Suicide Prevention, Adult, Biomedical Engineering, Depression, Female, Humans, Male, Middle Aged, Risk Factors, Speech Acoustics, Suicide, Case-Control Studies
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