Akustičke karakteristike glasa kod odraslih osoba sa depresivnim poremećajem

  • Gordana Calić Univerzitet u Beogradu, Fakultet za specijalnu edukaciju i rehabilitaciju
  • Mirjana Petrović-Lazić Univerzitet u Beogradu – Fakultet za specijalnu edukaciju i rehabilitaciju
  • Tatjana Mentus Univerzitet u Beogradu – Fakultet za specijalnu edukaciju i rehabilitaciju
  • Snežana Babac Univerzitet u Beogradu – Fakultet za specijalnu edukaciju i rehabilitaciju
Ključne reči: akustičke karakteristike, vokal, depresija, poremećaj glasa, vokalna analiza

Sažetak


Cilj ovog istraživanja bio je da proverimo i utvrdimo da li se akustičke karakteristike glasa razlikuju i diskriminišu grupu osoba sa depresivnim poremećajem (eksperimentalna grupa – EG) u odnosu na osobe iz tipične populacije (kontrolna grupa 1 – CG1) i u odnosu na osobe sa dijagnostikovanim psihogenim poremećajem glasa (kontrolna grupa 2 – CG2). Uzorkom je obuhvaćen 51 ispitanik (18 ispitanika EG, 24 ispitanika CG1 i 9 ispitanika CG2). Analizirano je devet akustičkih parametara na osnovu produženog foniranja vokala /a/. Za akustičku analizu korišćena je Kompjuterizovana laboratorija za akustičku analizu glasa i govora („Kay Elemetrics” Corp., model 4300), softverski program MDVP. Rezultati istraživanja pokazuju da se srednje vrednosti svih akustičkih parametara razlikuju između osoba sa depresivnim poremećajem u odnosu na obe kontrolne grupe i to: parametri Jitter, Shimmer, NHR, vAm, APQ i VTI imaju više vrednosti, a parametar SPI je niži u odnosu na obe grupe i F0 je niži u odnosu na CG1. Samo se parametar PPQ nije pokazao značajnim. Parametri Shimmer, vAm, APQ i VTI imaju najveću diskriminativnu vrednost za depresivni poremećaj. Akustičke karakteristike glasa analizirane na osnovu produženog foniranja vokala u ovom istraživanju razlikuju i diskriminišu EG i u odnosu na CG1 i u odnosu na CG2. U vokalnoj analizi parametri Shimmer, vAm, APQ i VTI bi potencijalno mogli biti markeri koji ukazuju na depresivni poremećaj.

Reference

Afshan, A., Guo, J., Park, S.J., Ravi, V., Flint, J., & Alwan, A. (2018, september). Effectiveness of voice quality features in detecting depression. In Interspeech 2018. ISCA, Hyderabad, India (pp. 1676–1680.) https://doi.org/10.21437/Interspeech.2018-1399.

Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., & Parker, G. (2013). Detecting depression: A comparison between spontaneous and read speech. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp.7547–7551).
https://doi.org/10.1109/ICASSP.2013.6639130

American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington DC: American Psychiatric Association.
https://doi.org/10.1176/appi.books.9780890425596.

Baek, Y.-S., Kim, S.-J., Kim, E., & Choi, Y. (2012). Vocal acoustic characteristics of speakers with depression. Korean Society of Speech Sciences, 4(1), 91–98.
https://doi.org/10.13064/KSSS.2012.4.1.091

Bueno-Notivol, J., Gracia-García, P., Olaya, B., Lasheras, I., López-Antón, R., & Santabárbara, J. (2021). Prevalence of depression during the COVID-19 outbreak: A meta-analysis of community-based studies. International Journal of Clinical and Health Psychology, 21(1), 100196.
https://doi.org/10.1016/j.ijchp.2020.07.007

Ćuk-Jovanović, L. (2002). Akustička analiza govornog signala pacijenata sa depresivnim poremećajem - karakteristike trajanja (The acoustic analysis of the speech signal of the patients with a depressive disorder: Characteristics of duration). Engrami, 24(2), 15–23.

Ćuk-Jovanović, L. (2003). Intenzitet govornog signala pacijenata sa depresivnim poremećajem (The intensity of the speech signal of the patients with a depressive disorder). Govor i jezik (pp.217–223). Beograd: Institut za eksperimentalnu fonetiku i patologiju govora.

Cummins, N., Epps, J., Breakspear, M., & Goecke, R. (2011). An Investigation of Depressed Speech Detection: Features and Normalization. Proceedings of the INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association. Florence, Italy: International Speech Communication Association (pp.2997–3000).
https://doi.org/10.21437/Interspeech.2011-750

Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T. F. (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.

Darby, J. K., Simmons, N., & Berger, P. A. (1984). Speech and voice parameters of depression: A pilot study. Journal of Communication Disorders, 17(2), 75–85.
https://doi.org/10.1016/0021-9924(84)90013-3,

Ellgring, H., & Scherer, R. (1996). Vocal indicators of mood change in depression. Journal of Nonverbal Behavior, 20(2), 83–110.
https://doi.org/10.1007/BF02253071.

Fuller, B. F., Horii, Y., & Conner, D. A. (1992). Validity and reliability of nonverbal voice measures as indicators of stressor-provoked anxiety. Research in Nursing & Health, 15(5), 379–389.
https://doi.org/10.1002/nur.4770150507

Hashim, N. W., Wilkes, M., Salomon, R., Meggs, J., & France, D. J. (2017). Evaluation of voice acoustics as predictors of clinical depression scores. Journal of Voice, 31(2), 256.e1–256.e6.
https://doi.org/10.1016/j.jvoice.2016.06.006

He, L., & Cao, C. (2018). Automated depression analysis using convolutional neural networks from speech. Journal of Biomedical Informatics, 83, 103–111.
https://doi.org/10.1016/j.jbi.2018.05.007

Heđever, M. (2012). Govorna akustika (Speech acoustics). Zagreb: Zagreb University, Faculty of Education and Rehabilitation Sciences

Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx). http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/d780dffbe8a381b25e1416884959e88b   Accessed February 2022.

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

Juslin, P. N., & Laukka, P. (2003). Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological Bulletin, 129(5), 770–814.
https://doi.org/10.1037/0033-2909.129.5.770

Kiss, G., & Jenei, A. Z. (2020). Investigation of the accuracy of depression prediction based on speech processing. 2020 43rd International Conference on Telecommunications and Signal Processing (TSP) (pp.129–132.)
https://doi.org/10.1109/TSP49548.2020.9163495

Kosztyła-Hojna, B., Moskal, D., Łobaczuk-Sitnik, A., Kraszewska, A., Zdrojkowski, M., Biszewska, J., & Skorupa, M. (2018). Psychogenic voice disorders. Otolaryngologia polska, 72(4), 26–34.
https://doi.org/10.5604/01.3001.0012.0636

Kuny, S., & Stassen, H. H. (1993). Speaking behavior and voice sound characteristics in depressive patients during recovery. Journal of Psychiatric Research, 27(3), 289–307.
https://doi.org/10.1016/0022-3956(93)90040-9

Lopez-Otero, P., & Docio-Fernandez, L. (2020). Analysis of gender and identity issues in depression detection on de-identified speech. Computer Speech & Language, 101118.
https://doi.org/10.1016/j.csl.2020.101118

Low, L.-S. A., Maddage, M. C., Lech, M., Sheeber, L. B., & Allen, N. B. (2011). Detection of clinical depression in adolescents' speech during family interactions. IEEE Transactions on Biomedical Engineering, 58(3), 574–586.
https://doi.org/10.1109/TBME.2010.2091640

Milutinovic, Z. (1997). Klinički atlas poremećaja glasa: Teorija i praksa [Clinical atlas of voice disorders: Theory and practice]. Belgrade: Institute for textbook publishing and teaching aids.

Moore, E. I. I., Clements, M., Peifer, J., & Weisser, L. (2004). Comparing objective feature statistics of speech for classifying clinical depression. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1, 17–20.
https://doi.org/10.1109/IEMBS.2004.1403079

Mundt, J. C., Snyder, P. J., Cannizzaro, M. S., Chappie, K., & Geralts, D. S. (2007). Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology. Journal of Neurolinguistics, 20(1), 50–64.
https://doi.org/10.1016/j.jneuroling.2006.04.001

Mundt, J. C., Vogel, A. P., Feltner, D. E., & Lenderking, W. R. (2012). Vocal acoustic biomarkers of depression severity and treatment response. Biological Psychiatry, 72(7), 580–587.
https://doi.org/10.1016/j.biopsych.2012.03.015

Nilsonne, A. (1988). Speech characteristics as indicators of depressive illness. Acta Psychiatrica Scandinavica, 77(3), 253–263.
https://doi.org/10.1111/j.1600-0447.1988.tb05118.x

Nunes, A., Coimbra, R. L., & Teixeira, A. (2010). Voice quality of European Portuguese emotional speech. Computational Processing of the Portuguese Language, International Conference on Computational Processing of the Portuguese Language, 6001, (pp.142–151.)
https://doi.org/10.1007/978-3-642-12320-7_19

Ozdas, A., Shiavi, R. G., Silverman, S. E., Silverman, M. K., & Wilkes, D. 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. G., Silverman, S. E., Silverman, M. K., & Wilkes, D. M. (2000). Analysis of fundamental frequency for near term suicidal risk assessment. SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. "Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions", 5, 1853–1858.
https://doi.org/10.1109/ICSMC.2000.886379

Patel, S., & Scherer, K. R. (2013). Vocal behaviour. In: Hall JA, Knapp ML, editors. Handbook of nonverbal communication. Berlin: Mouton-DeGruyter (pp.167–204.)
https://doi.org/10.1515/9783110238150.167

Petrović-Lazić, M., Babac, S., Ivanković, Z., & Kosanović, R. (2009). Multidimenzionalna akustička analiza patološkog glasa (Multidimensional Acoustic Analysis of Pathological Voice). Srpski arhiv za celokupno lekarstvo, 137(5-6), 234–238.
https://doi.org/10.2298/SARH0906234P

Petrović-Lazić, M., Jovanović, N., Kulić, N., Babac, S., & Jurisić, V. (2014). Acoustic and perceptual characteristics of the voice in patients with vocal polyps after surgery and voice therapy. Journal of Voice, 29(2), 241–246.
https://doi.org/10.1016/j.jvoice.2014.07.009

Popović, M. (2003). Akustičke karakteristike govora i psihološko-emocionalni faktori (Acoustic characteristics of speech and psychological-emotional factors). Govor i jezik (pp.210–216.), Beograd: Institut za eksperimentalnu fonetiku i patologiju govora

Quatieri, T., & Malyska, N. (2012). Vocal-source biomarkers for depression: A link to psychomotor activity, In Interspeech 2012, 13th Annual Conference of the International Speech Communication Association Portland, OR, USA
https://doi.org/10.21437/Interspeech.2012-311

Rejaibi, E., Komaty, A., Meriaudeau, F., Agrebi, S., & Othmani, A. (2022). MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech. Biomedical Signal Processing and Control, 71, 103107.
https://doi.org/10.1016/j.bspc.2021.103107

Ritchie, H. & Roser, M. (2018). Mental Health. Our World in Data. https://ourworldindata.org/mental-health  Accessed June 2022.

Roussel, N. C., & Lobdell, M. (2006). The clinical utility of the soft phonation index. Clinical Linguistics & Phonetics, 20(2-3), 181–186.
https://doi.org/10.1080/02699200400026942

Sahu, S., & Espy-Wilson, C. (2016). Speech features for depression detection. The Interspeech 2016, 17th Annual Conference of the International Speech Communication Association (pp.1928–1932.)
https://doi.org/10.21437/Interspeech.2016-1566

Scherer, K. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication, 40(1-2), 227–256.
https://doi.org/10.1016/S0167-6393(02)00084-5

Scherer, K. R. (1986). Vocal affect expression: A review and a model for future research. Psychological Bulletin, 99(2), 143–165.
https://doi.org/10.1037/0033-2909.99.2.143

Scherer, K. R., Clark-Polner, E., & Mortillaro, M. (2011). In the eye of the beholder? Universality and cultural specificity in the expression and perception of emotion. International Journal of Psychology, 46(6), 401–435.
https://doi.org/10.1080/00207594.2011.626049

Silva, W. J., Lopes, L., Galdino, M. K. C., & Almeida, A. A. (2021). Voice acoustic parameters as predictors of depression. Journal of Voice, Article in Press
https://doi.org/10.1016/j.jvoice.2021.06.018

Sturim, D.E., Torres-Carrasquillo, P.A., Quatieri, T., & Malyska, N. (2011). Automatic detection of depression in speech using Gaussian Mixture Modeling with factor analysis. Interspeech 2011, 12th Annual Conference of the International Speech Communication Association, Florence, Italy
https://doi.org/10.21437/Interspeech.2011-746

Taguchi, T., Tachikawa, H., Nemoto, K., Suzuki, M., Nagano, T., Tachibana, R., Nishimura, M., & Arai, T. (2018). Major depressive disorder discrimination using vocal acoustic features. Journal of Affective Disorders, 225, 214–220.
https://doi.org/10.1016/j.jad.2017.08.038

Teixeira, J. P., & Fernandes, P. O. (2015). Acoustic analysis of vocal dysphonia. Procedia Computer Science, 64, 466–473.
https://doi.org/10.1016/j.procs.2015.08.544

Wang, J., Zhang, L., Liu, T., Pan, W., Hu, B., & Zhu, T. (2019). Acoustic differences between healthy and depressed people: a cross-situation study. BMC Psychiatry, 19(1).
https://doi.org/10.1186/s12888-019-2300-7

World Health Organization‎. Depression and other common mental disorders: Global health estimates. World Health Organization; 2017. http://www.who.int/iris/handle/10665/254610 Accessed August 2021.

Xing, Y., Liu, Z., Li, G. Ding, Z., & Hu, B. (2022). 2-level hierarchical depression recognition method based on task-stimulated and integrated speech features. Biomedical Signal Processing and Control, 72, 103287.
https://doi.org/10.1016/j.bspc.2021.103287

Yang, B., & Lugger, M. (2010). Emotion recognition from speech signals using new harmony features. Signal Processing, 90(5), 1415–1423.
https://doi.org/10.1016/j.sigpro.2009.09.009

Yang, Y., Fairbairn, C., & Cohn, J. F. (2013). Detecting depression severity from vocal prosody. IEEE Transactions on Affective Computing, 4(2), 142–150.
https://doi.org/10.1109/T-AFFC.2012.38

Zwetsch, I., Fagundes, R., Russomano, T., & Scolari, D. (2006). Digital signal processing in the differential diagnosis of benign larynx diseases. Scientia Medica, 16(3), 109.

Objavljeno
2022/12/30
Rubrika
Naučni članci