Conducta suicida y los parámetros acústicos de la voz y el habla. Revisión sistemática
Main Article Content
Abstract
Suicide is a worldwide health problem, with suicidal behavior being one of the predictors of suicide mortality. However, its assessment still remains complex. Although there is a large body of literature that has addressed different perspectives of suicidal behavior, new methods that allow a rapid and objective assessment are needed to provide clinicians and patients with an evaluation system that dynamically records changes in emotional states. This is why we sought to evaluate through the literature the usefulness of voice and speech measures in the detection and follow-up of suicidal behavior. This was done through a search of scientific literature in different databases: PubMed, Web of Science, IEEE Xplore, yielding1125 articles. Among the results obtained it was possible to observe that the most used tasks to evaluate voice and speech in suicidal behavior are those of free expression such as the interview and text reading. Among the methods of analysis, these can be grouped into those that seek to establish differences by contrasting acoustic measures between groups and those that use classification systems. The evidence shows a link between acoustic parameters of voice and speech and suicidal behavior and also their usefulness in the follow-up of suicidal behavior. Therefore, it can be concluded that voice and speech parameters are associated with suicidal behavior, which would indicate that this information could be used as biomarkers of suicidal behavior allowing dynamic and remote detection of suicidal risk.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
La RACC aplicará la licencia internacional de atribuciones comunes creativas (Reconocimiento 4.0 Internacional: https://creativecommons.org/licenses/by/4.0/).
Bajo esta licencia, se permite cualquier explotación de la obra, incluyendo la explotación con fines comerciales y la creación de obras derivadas, la distribución de las cuales también está permitida sin ninguna restricción. Esta licencia es una licencia libre según la Freedom Defined. La única condición es que siempre y en todos los casos se cite a los autores y a la fuente original de publicación (i.e., RACC). Esta licencia fue desarrollada para facilitar el acceso abierto, gratuito y libre a trabajos originales científicos y artísticos.
How to Cite
References
Akkaralaertsest, T., & Yingthawornsuk, T. (30 de octubre – 1 de noviembre de 2019). Classification of Depressed Speech Samples with Spectral Energy Ratios as Depression Indicator. Proceedings of 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing, ISAI-NLP 2019, Chiang Mai, Tailandia. https://doi.org/10.1109/iSAI-NLP48611.2019.9045167
Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., & Parker, G. (26-31 de mayo de 2013). Detecting depression: A comparison between spontaneus and read speech. Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canadá. https://doi.org/10.1109/ICASSP.2013.6639130
Belouali, A., Gupta, S., Sourirajan, V., Yu, J., Allen, N., Alaoui, A., Dutton, M., & Reinhard, M. (2021). Acoustic and language analysis of speech for suicidal ideation among US veterans. BioData Mining, 14(1), 1-17. https://doi.org/10.1186/s13040-021-00245-y
Brown, S., Iqbal, Z., Burbidge, F., Sajjad, A., Reeve, M., Ayres, V., Melling, R., & Jobes, D. (2020). Embedding an evidence-based model for suicide prevention in the national health service: A service improvement initiative. International Journal of Environmental Research and Public Health, 17(14), 4920. https://doi.org/10.3390/ijerph17144920
Burke, T., Ammerman, B., & Jacobucci, R. (2019). The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of Affective Disorders, 245, 869–884. https://doi.org/10.1016/j.jad.2018.11.073
Chu, C., Buchman-Schmitt, J. M., Stanley, I. H., Hom, M. A., Tucker, R. P., Hagan, C. R., Rogers, M. L., Podlogar, M. C., Chiurliza, B., Ringer, F. B., Michaels, M. S., Patros, C. H. G., & Joiner, T. E. (2017). The interpersonal theory of suicide: A systematic review and meta-analysis of a decade of cross-national research. Psychological Bulletin, 143(12), 1313–1345. https://doi.org/10.1037/bul0000123
Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10-49. https://doi.org/10.1016/j.specom.2015.03.004
Dietrich, M., Andreatta, R. D., Jiang, Y., & Stemple, J. C. (2020). Limbic and cortical control of phonation for speech in response to a public speech preparation stressor. Brain Imaging and Behavior, 14(5), 1696-1713. https://doi.org/10.1007/s11682-019-00102-x
Figueroa, C., Otzen, T., Alarcón, C., Ríos, A., Frugone, D., & Lagos, R. (2020). Association between suicidal ideation and acoustic parameters of university students’ voice and speech: a pilot study. Logopedics Phoniatrics Vocology, 46(2), 55-62. https://doi.org/10.1080/14015439.2020.1733075
Fowler, J. (2012). Suicide risk assessment in clinical practice: pragmatic guidelines for imperfect assessments. Psychotherapy, 49(1), 81-90. https://doi.org/10.1037/a0026148
France, D., & Shiavi, R. (2000). Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Transactions on Biomedical Engineering, 47(7), 829-837. https://doi.org/10.1109/10.846676
Hashim, N., Wilkes, M., Salomon, R., Meggs, J., & France, D. (2016). Evaluation of Voice Acoustics as Predictors of Clinical Depression Scores. Journal of Voice, 31(2), P256.E1-256.E6. https://doi.org/10.1016/j.jvoice.2016.06.006
Jiang, H., Hu, B., Liu, Z., Wang, G., Zhang, L., Li, X., & Kang, H. (2018). Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features. Computational and Mathematical Methods in Medicine, 6508319. https://doi.org/10.1155/2018/6508319
Jiang, H., Hu, B., Liu, Z., Yan, L., Wang, T., Liu, F., Kang, H., & Li, X. (2017). Investigation of different speech types and emotions for detecting depression using different classifiers. Speech Communication, 90, 39-46. https://doi.org/10.1016/j.specom.2017.04.001
Joiner, T. (2005). Why people die by suicide (1st Ed.). Harvard University Press.
Joiner, T., Jeon, M., Lieberman, A., Janakiraman, R., Duffy, M., Gai, M., & Dougherty, S. (2021). On prediction, refutation, and explanatory reach: A consideration of the Interpersonal Theory of Suicidal Behavior. Preventive Medicine,152(1), 106453. https://doi.org/10.1016/j.ypmed.2021.106453.
Kiss, G., & Vicsi, K. (2017). Mono- and multi-lingual depression prediction based on speech processing. International Journal of Speech Technology, 20(4), 919–935. https://doi.org/10.1007/s10772-017-9455-8
Liberati, A., Altman, D., Tetzlaff, J., Mulrow, C., Gøtzsche, P., Ioannidis, J., Clarke, M., Devereaux, P., Kleijnen, J., & Moher, D. (2009). The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. Plos Medicine, 6(7), e1000100. https://doi.org/10.1371/journal.pmed.1000100
López, M., & Medina, V. (2016). Efecto de la expresión de emociones básicas sobre los parámetros acústicos y los formantes vocálicos en profesionales de la voz de la ciudad de Concepción, Chile, año 2016. (Tesis de Grado, Universidad del Desarrollo, Chile). Repositorio Universidad del Desarrollo. https://repositorio.udd.cl/bitstream/handle/11447/1300/Documento.pdf?sequence=1&isAllowed=y
Low, L., Maddage, N., Lech, M., & Allen, N. (15-17 de junio de 2009). Mel frequency cepstral feature and Gaussian Mixtures for modeling clinical depression in adolescents. Proceedings of 8th IEEE International Conference on Cognitive Informatics, Hong Kong, China. https://doi.org/10.1109/COGINF.2009.5250714
Miller, D. N. (2018). Understanding and preventing youth suicide: Ideation-to-action theories of suicidal behavior and their implications for school-based suicide prevention. En P. Terry & R. Price (Eds.), Understanding suicide: Perspectives, risk factors and gender differences (pp. 165–185). Nova Science Publishers.
Mitra, V., Shriberg, E., Vergyri, D., Knoth, B., & Salomon, R. (19-24 de abril de 2015). Cross-corpus depression prediction from speech. Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia. https://doi.org/10.1109/ICASSP.2015.7178876
O’Carroll, P., Berman, A., Maris, R., Moscicki, E., Tanney, B., & Silverman, M. (1996). Beyond the Tower of Babel: a nomenclature for suicidology. Suicide & Life-Threatening Behavior, 26(3), 237–252. https://doi.org/10.1007/0-306-47150-7_7
Organización Mundial de la Salud, OMS (2021). Suicidio. https://www.who.int/es/news-room/fact-sheets/detail/suicide
Organización Panamericana de la Salud (2021). Prevención del suicidio. https://www.paho.org/es/temas/prevencion-suicidio
Ozdas, A., Shiavi, R., Silverman, S., Silverman, M., & Wilkes, D. (8-11 de octubre de 2000). Analysis of fundamental frequency for near term suicidal risk assessment. Proceedings of 2000 IEEE International Conference on Systems, Man and Cybernetics, Nashville, Estados Unidos. https://doi.org/10.1109/icsmc.2000.886379
Ozdas, A., Shiavi, R., Silverman, S., Silverman, M., & Wilkes, M. (2004). Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Transactions on Biomedical Engineering, 51(9), 1530–1540. https://doi.org/10.1109/TBME.2004.827544
Ozdas, A., Shiavi, R., Wilkes, D., Silverman, M., & Silverman, S. (2004). Analysis of Vocal Tract Characteristics for Near-term Suicidal Risk Assessment. Methods of Information in Medicine, 43(1), 36–38. https://doi.org/10.1055/s-0038-1633420
Parekh, P., & Patil, M. (1-2 de agosto de 2017). Clinical Depression Detection for Adolescent by speech Features. Proceedings of 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India. https://doi.org/10.1109/ICECDS.2017.8390102
Scherer, S., Pestian, J., & Morency, L. (19-24 de abril de 2015). Reduced vowel space is a robust indicator of psychological distress: a cross-corpus analysis. Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, South Brisbane, Australia. https://doi.org/10.1109/ICASSP.2015.7178880
Scherer, S., Pestian, J., & Morency, L. (26-31 de mayo de 2013). Investigating the speech characteristics of suicidal adolescents. Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canadá. https://doi.org/10.1109/ICASSP.2013.6637740
Silverman, M., Berman, A., Sanddal, N., O’Carroll, P., & Joiner, T. (2007). Rebuilding the Tower of Babel: A Revised Nomenclature for the Study of Suicide and Suicidal Behaviors Part 1: Background, Rationale, and Methodology. Suicide and Life-Threatening Behavior, 37(3), 248–263. https://doi.org/10.1521/suli.2007.37.3.248
Subari, K., Wilkes, D., Shiavi, R., Silverman, S., & Silverman, M. (30 de noviembre–2 de diciembre de 2010). Comparison of speaker normalization techniques for classification of emotionally disturbed subjects based on voice. Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malasia. https://doi.org/10.1109/IECBES.2010.5742248
Suwannakhun, S., & Yingthawornsuk, T. (30 de octubre–1 de noviembre de 2019). Characterizing Depressive Related Speech with MFCC. Proceedings of 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing, ISAI-NLP, Chiang Mai, Tailandia. https://doi.org/10.1109/iSAI-NLP48611.2019.9045499
Tonn, P., Degani, Y., Hershko, S., Klein, A., Seule, L., & Schulze, N. (2020). Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study. JMIR Research Protocols, 9(5), e13852. https://doi.org/10.2196/13852
Van Puyvelde, M., Neyt, X., McGlone, F., & Pattyn, N. (2018). Voice Stress Analysis: A New Framework for Voice and Effort in Human Performance. Frontiers in Psychology, 9, 1994. https://doi.org/10.3389/fpsyg.2018.01994
Yingthawornsuk, T., & Shiavi, R. (14-17 de octubre de 2008). Distinguishing depression and suicidal risk in men using GMM based frequency contents of affective vocal tract response. Proceedings of 2008 International Conference on Control, Automation and Systems (ICCAS), Seúl, Corea del Sur. https://doi.org/10.1109/ICCAS.2008.4694621
Yingthawornsuk, T. (29 de noviembre–1 de diciembre de 2016). Spectral Entropy in Speech for Classification of Depressed Speakers. Proceedings of 12th International Conference on Signal Image Technology and Internet-Based Systems, Nápoles, Italia. https://doi.org/10.1109/SITIS.2016.113
Zalsman, G., Hawton, K., Wasserman, D., Van Heering, K., Arensman, E., Sarchiapone, M., Carli, V., Hoschl, C., Barzilay, R., Balazs, J., Purebl, G., Kajn, J., Sáiz, P., Lipsicas, C., Bobes, J., Cozman, D., Hegerl, U., & Zohar, J. (2016). Suicide prevention strategies revisited: 10-year systematic review. Lancet Psychiatry, 3(7), 646-59. https://doi.org/10.1016/S2215-0366(16)30030-X
Zhang, L., Duvvuri, R., Chandra, K., Nguyen, T., & Ghomi, R. (2020). Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative. Depression and Anxiety, 37(7), 657–669. https://doi.org/10.1002/da.23020