Investigating the Course of Recovery in High Risk Suicide using Power Spectral Density
Keywords:
Speech, suicide, depression, severityAbstract
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.
Â
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.
Downloads
Published
Issue
Section
License
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-NonCommercial 4.0 International that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
