Timing patterns of speech as potential indicators of near-term suicidal risk

In an effort to find a reliable method that could assist clinicians in risk assessment, information in the speech signal has been found to contain characteristic changes associated with high risk suicidal states. This paper addresses the questions of (1) Does information contain in the speech timing-based measures able to discriminate between high risk suicidal (HR) speech from the depressed (DP) speech. (2) How well do speech features, specifically the timing-based measures can predict the ratings from a well-known medical diagnostic tool known as the Hamilton Depression Rating Scale (HAMD). In the first study, using the leave-one-out procedure as a means to measure a classifier performance for all-data classification revealed a single speech timing-based measure to be a significant discriminator with 79% overall correct leave-one-out classification in male (MR) and female (FR) reading speech from Database A. For male patients, using the trained features on Database A and testing on Database B1 successfully demonstrated up to 100% detection of high risk speech in Database B1. In the second study, the acoustic measurements were shown to effectively predict the HAMD score with less than 5% mean absolute error using only combinations from the timing-based measures and eliminating all spectrum-based measures

[1]  M. Landau Acoustical Properties of Speech as Indicators of Depression and Suicidal Risk , 2008 .

[2]  Fernando De la Torre,et al.  Detecting depression from facial actions and vocal prosody , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[3]  Nik Wahidah Hashim,et al.  Analysis of Timing Pattern of Speech as Possible Indicator for Near-Term Suicidal Risk and Depression in Male Patients , .

[4]  Donatella Marazziti,et al.  Cognitive impairment in major depression. , 2010, European journal of pharmacology.

[5]  Michael Cannizzaro,et al.  Voice acoustical measurement of the severity of major depression , 2004, Brain and Cognition.

[6]  Mohammad H. Mahoor,et al.  Social risk and depression: Evidence from manual and automatic facial expression analysis , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[7]  D. Klatt,et al.  Analysis, synthesis, and perception of voice quality variations among female and male talkers. , 1990, The Journal of the Acoustical Society of America.

[8]  C. Bradshaw,et al.  Elongation of Pause-Time in Speech: A Simple, Objective Measure of Motor Retardation in Depression , 1976, British Journal of Psychiatry.

[9]  G. H. Monrad‐Krohn,et al.  The third element of speech: prosody in the neuro-psychiatric clinic. , 1957, The Journal of mental science.

[10]  Å. Nilsonne Speech characteristics as indicators of depressive illness , 1988, Acta psychiatrica Scandinavica.

[11]  Hande Kaymaz-Keskinpala,et al.  Screening for high risk suicidal states using mel-cepstral coefficients and energy in frequency bands , 2007, 2007 15th European Signal Processing Conference.

[12]  Melonie P. Heron Deaths: leading causes for 2008. , 2010, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[13]  David A. Landgrebe,et al.  Predicting the Required Number of Training Samples , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  H Hollien,et al.  [Vocal and speech patterns of depressive patients]. , 1977, Folia phoniatrica.

[15]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[16]  Wouter Hulstijn,et al.  Psychomotor symptoms in depression: a diagnostic, pathophysiological and therapeutic tool. , 2008, Journal of affective disorders.

[17]  D. Widlöcher,et al.  Decision Time and Movement Time in Depression: Differential Effects of Practice before and after Clinical Improvement , 1989, Perceptual and motor skills.

[18]  Thomas F. Quatieri,et al.  Phonologically-based biomarkers for major depressive disorder , 2011, EURASIP J. Adv. Signal Process..

[19]  M. Alpert,et al.  Reflections of depression in acoustic measures of the patient's speech. , 2001, Journal of affective disorders.

[20]  Jeffrey F. Cohn,et al.  Detecting Depression Severity from Vocal Prosody , 2013, IEEE Transactions on Affective Computing.

[21]  Albert A. Rizzo,et al.  Automatic behavior descriptors for psychological disorder analysis , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[22]  Roland Göcke,et al.  Relative Body Parts Movement for Automatic Depression Analysis , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[23]  M. Hamilton A RATING SCALE FOR DEPRESSION , 1960, Journal of neurology, neurosurgery, and psychiatry.

[24]  J. Mendlewicz,et al.  Speech Pause Time as a Method for the Evaluation of Psychomotor Retardation in Depressive Illness , 1985, British Journal of Psychiatry.

[25]  Michael Wagner,et al.  Head Pose and Movement Analysis as an Indicator of Depression , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[26]  K. Scherer Vocal affect expression: a review and a model for future research. , 1986, Psychological bulletin.

[27]  Å. Nilsonne,et al.  Acoustic analysis of speech variables during depression and after improvement , 1987, Acta psychiatrica Scandinavica.

[28]  Roland Göcke,et al.  Can body expressions contribute to automatic depression analysis? , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[29]  R. Lane Cognitive and Psychomotor Effects of Antidepressants with Emphasis on Selective Serotonin Reuptake Inhibitors and the Depressed Elderly Patient , 2000 .

[30]  Jan Fawcett,et al.  Clinical correlates of inpatient suicide. , 2003, The Journal of clinical psychiatry.

[31]  Constantine Kotropoulos,et al.  Emotional speech recognition: Resources, features, and methods , 2006, Speech Commun..

[32]  Klaus R. Scherer,et al.  Vocal indicators of mood change in depression , 1996 .

[33]  R Jouvent,et al.  Speech pause time and the retardation rating scale for depression (ERD). Towards a reciprocal validation. , 1984, Journal of affective disorders.

[34]  D. Mitchell Wilkes,et al.  Direct acoustic feature using iterative EM algorithm and spectral energy for classifying suicidal speech , 2007, INTERSPEECH.

[35]  J. Mundt,et al.  Vocal Acoustic Biomarkers of Depression Severity and Treatment Response , 2012, Biological Psychiatry.

[36]  J. Mundt,et al.  Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology , 2007, Journal of Neurolinguistics.

[37]  Elliot Moore,et al.  Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech , 2008, IEEE Transactions on Biomedical Engineering.

[38]  D. Mitchell Wilkes,et al.  Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk , 2004, IEEE Transactions on Biomedical Engineering.