Investigating the Course of Recovery in High Risk Suicide using Power Spectral Density

Authors

  • Nik Nur Wahidah N. H. Department of Mechatronics Engineering, International Islamic University Malaysia, Selangor
  • Mitch D. Wilkes
  • Ronald M. Salomon

Keywords:

Speech, suicide, depression, severity

Abstract

This pilot study attempts to address the question of whether there is a statistically significant change in patients’ vocal characteristics as they progress from the initial recording session (labeled as high risk suicidal) to the second and third recording session that was collected a few days after receiving treatments. Interview speech and reading speech were collected from five male patients and eight female patients. Using the Power Spectral Density (PSD) feature, the normalized Euclidean distance from each vector to the separating hyperplane was measured in order to capture the progression or regression of patients after three treatment sessions. One-tailed paired sample t-test demonstrates statistically significant difference between the first and second session which represents improvement. In some cases, the first and third session also demonstrates statistically significant difference which shows that patients does not experiencing relapse.

 

References

K. A. Busch, J. Fawcett, D. G. Jacob, “Clinical Correlates of Inpatient Suicideâ€, Psychiatry Ann, 1993.

S. E. Silverman, M. K. Silverman, “Method and Apparatus for Evaluating Near-Term Suicidal Risk using Vocal Parameters†U.S patent 7 062 443, June 13, 2006.

D. J. France, R. G. Shiavi, S. E. Silverman, M. K. Silverman, D. M. Wilkes, “Acoustical Properties of Speech as Indicators of Depression and Suicidal Riskâ€, IEEE Transaction on Biomedical Engineering, vol. 47, no. 7, 2000.

A. Ozdas, R. G. Shiavi, S. E. Silverman, M. K. Silverman, D. M. Wilkes, “Investigation of Vocal Jitter and Glottal Flow Spectrum as Possible Cues for Depression and Near-Term Suicidal Riskâ€, IEEE Transaction on Biomedical Engineering, vol. 51, no. 9, 2004.

T. Yingthawornsuk, H. K. Keskinpala, D. M. Wilkes, R. G. Shiavi, R. M. Salomon, “Direct Acoustic Feature using Iterative EM Algorithm and Spectral Energy for Classifying Suicidal Speechâ€, INTERSPEECH, pp. 766-769, 2007.

H. K. Keskinpala, T. Yingthawornsuk, D. M. Wilkes, R. G. Shiavi, R. M. Salomon, “Screening for High Risk Suicidal States using Mel-Cepstral Coefficients and Energy in Frequency Bandsâ€, In European Signal Processing Conf., pp. 2229-2233, 2007.

N. N. Wahidah, D. M. Wilkes, R. M. Salomon, J. Meggs, Analysis of Timing Pattern of Speech as Possible Indicator for Near-Term Suicidal Risk and Depression in Male Patients, International Conference on Signal Processing Systems (ICSPS 2012), vol. 28, 2012..

S. Theodoridis, K. Koutroumbas, "Pattern Recognition, Fourth Edition" Ac-ic Press, 2008

A. Nilsonne, “Acoustic analysis of speech variables during depression and after improvementâ€,, vol. 76, pp. 235-245, 1987.

A. Nilsonne, J. Sundberg. S. TcrnstrBm, A. Askcnfelt, “Measuring the rate of change of fundamental frequency in fluent speech during mental deprcssionâ€. J. of the Acoustical Society of America, vol. 83, no. 2, pp. 716 728, pp. 1988.

F. Tolkmitt, H. Helfrich, R. Standke, K. R. Scherer, “Vocal indicators of psychiatric treatment effects in depressives and schizophrenicsâ€, J. of Common Disorders, vol. 15, pp. 209-222, 1982.

E. Szabadi, C. M. Bradshaw, J. A. Besson, “Elongation of pause-time in speech: a simple objective measure of motor retardation in depressionâ€, British J. of Psychiatry, vol. 129, pp. 592-597, 1976.

H. Ellgring and K.R. Scherer, “Vocal Indicators of Mood Change in Depression,†J. Nonverbal Behavior, vol. 20, no. 2, pp. 83-110, 1996.

S. Kuny and H. Stassen, “Speaking Behavior and Voice Sound Characteristics in Depressive Patients during Recovery,†J Psychiatr Res., vol. 27, no. 3, 1993.

J.C. Mundt, P.J. Snyder, M.S. Cannizzaro, K. Chappie, and D.S. Geraltsa, “Voice Acoustic Measures of Depression Severity and Treatment Response Collected via Interactive Voice Response (IVR) Technology,†J. Neurolinguistics, vol. 20, pp. 50-64, 2007.

Ying Yang, F. Catherine, J. F. Cohn, “Detecting Depression Severity from Vocal Prosodyâ€, IEEE Trans. On Affective Computing, vol. 4, no. 2, 2013.

J. K. Darby, H. Hollien, “Vocal and Speech Patterns of Depressive Patientsâ€, vol. 29, pp. 279-291, 1977.

S. J. Blumenthal, D. J. Kupter , “Suicide over the Life Cycle: Risk Factors, Assessment, and Treatment of Suicidal Patientsâ€, pg.111.

R. I. Simon, “Preventing Patient Suicide: Clinical Assessment and Managementâ€, Washington, DC, American Psychiatric Publishing, 2011.

P. Hardy, R. Jouvent, D. Widlocher, Speech Pause Time and the Retardation Rating Scale for Depression (ERD), Towards a Reciprocal Validation, Journal of Affective Disorders (1984)

E. Szabadi, C. M. Bradshaw, J. A. O. Besson, “Elongation of Pause-Time in Speech: A simple, Objective Measure of Motor Retardation in Depressionâ€, The British J. of Psych., vol. 129, no. 7, pp. 592-597, 1976.

H. Ellgring, K. R. Scherer, “Vocal Indication of Mood Change in Depressionâ€, J. of Nonverbal Behavior, vol. 20, no. 2, pp. 83-110, 1996.

H. H. Stasen, S. Kuny, D. Hell, The Speech Analysis Approach to Determining Onset of Improvement Under Antidepressants, European Neuropsychopharmachology, 8, 303-310 (1998)

M. Cannizaro, B. Harel, N. Reilly, P. Chappell, P. J. Snyder, Voice Acoustical Measurement of the Severity of Major Depression, Brain and Cognition, 56(1), pp.30-35, (2004).

J. C. Mundt, P. J. Snyder, M. S. Cannizzaro, K. Chappie, D. S. Geralts, Voice Acoustic Measures of Depression Severity and Treatment Response Collected Via Interactive Voice Response (IVR) Technology, Journal of Neurolinguistic, 20, 50-64 (2007)

J. C. Mundt, A. P. Vogel, D. E. Feltner, W. R. Lenderking, Vocal Acoustic Biomarkers of Depression Severity and Treatment Response, Journal of Biological Psychiatry, 72, 580-587 (2012)

International Phonetic Association, Phonetic description and the IPA chart, Handbook of the International Phonetic Association: a guide to the use of international phonetic alphabet, (Cambridge University Press, 1999 in press)

H. M. Kalayeh, D. A. Landgrebe, “Predicting the required number of training samples, Pattern analysis and machine intelligenceâ€, IEEE transaction on Pattern Analysis and Machine Learning, vol. 5, no.6, pp. 664-667, 1983.

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Published

2015-08-17

How to Cite

N. H., N. N. W., Wilkes, M. D., & Salomon, R. M. (2015). Investigating the Course of Recovery in High Risk Suicide using Power Spectral Density. Asian Journal of Applied Sciences, 3(4). Retrieved from https://ajouronline.com/index.php/AJAS/article/view/2836

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