Marilyn K. Silverman

Acoustical properties of speech as indicators of depression and suicidal risk

Acoustic properties of speech have previously been identified as possible cues to depression, and there is evidence that certain vocal parameters may be used further to objectively discriminate between depressed and suicidal speech. Studies were performed to analyze and compare the speech acoustics of separate male and female samples comprised of normal individuals and individuals carrying diagnoses of depression and high-risk, near-term suicidality. The female sample consisted of ten control subjects, 17 dysthymic patients, and 21 major depressed patients. The male sample contained 24 control subjects, 21 major depressed patients, and 22 high-risk suicidal patients. Acoustic analyses of voice fundamental frequency (F/sub 0/), amplitude modulation (AM), formants, and power distribution were performed on speech samples extracted from audio recordings collected from the sample members. Multivariate feature and discriminant analyses were performed on feature vectors representing the members of the control and disordered classes. Features derived from the formant and power spectral density measurements were found to be the best discriminators of class membership in both the male and female studies. AM features emerged as strong class discriminators of the male classes. Features describing F/sub 0/ were generally ineffective discriminators in both studies. The results support theories that identify psychomotor disturbances as central elements in depression and suicidality.

Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk

Among the many clinical decisions that psychiatrists must make, assessment of a patients risk of committing suicide is definitely among the most important, complex, and demanding. When reviewing his clinical experience, one of the authors observed that successful predictions of suicidality were often based on the patients voice independent of content. The voices of suicidal patients judged to be high-risk near-term exhibited unique qualities, which distinguished them from nonsuicidal patients. We investigated the discriminating power of two excitation-based speech parameters, vocal jitter and glottal flow spectrum, for distinguishing among high-risk near-term suicidal, major depressed, and nonsuicidal patients. Our sample consisted of ten high-risk near-term suicidal patients, ten major depressed patients, and ten nondepressed control subjects. As a result of two sample statistical analyses, mean vocal jitter was found to be a significant discriminator only between suicidal and nondepressed control groups (p<0.05). The slope of the glottal flow spectrum, on the other hand, was a significant discriminator between all three groups (p<0.05). A maximum likelihood classifier, developed by combining the a posteriori probabilities of these two features, yielded correct classification scores of 85% between near-term suicidal patients and nondepressed controls, 90% between depressed patients and nondepressed controls, and 75% between near-term suicidal patients and depressed patients. These preliminary classification results support the hypothesized link between phonation and near-term suicidal risk. However, validation of the proposed measures on a larger sample size is necessary.

Methods for evaluating near-term suicidal risk using vocal parameters

A method for evaluating near-term suicidal risk by analysis of a series of spoken words includes converting the spoken series of words into a signal having characteristics indicative of said words as spoken, dynamically monitoring said signal to detect changes therein and identifying the person as having a relatively high near-term risk of suicide on the basis of such monitored changes in the signal relative to the speech of individuals in good mental health having no near-term suicidal risk.

Acoustical correlates of near‐term suicidal risk

In the course of many years of clinical work in emergency rooms and office consultation with suicidal patients, clinicians have often successfully predicted suicidality based on the vocal patterns of the patients, independent of the content. Vocal sound and clinical substance reciprocally augmented each other in determining the near‐term risk [M. K. Silverman and S. E. Silverman, ‘‘From sound to silence: A preliminary investigation of the use of vocal parameters in the prediction of near‐term suicidal risk,’’ submitted to J. Med. Psychotheraphy]. Vocal patterns heard as representing a ‘‘hollow,’’ ‘‘toneless’’ sound were designated unanimously as the most compelling feature in suicidal voices. Motivated by qualitative descriptions of experienced clinicians, a quantitative study was carried out that investigated the acoustic correlates of near‐term risk. The audio tapes selected for this research were suicide notes left on tapes donated by survivors, recordings of several patients who had been hospitalized,...

Comparison of speaker normalization techniques for classification of emotionally disturbed subjects based on voice

When reviewing his clinical experience in treating suicidal patients, one of the authors observed that successful predictions of suicidality were often based on the patients voice independent of content. Research has shown that the Gaussian mixture model of the mel-cepstral features of speech can be used to distinguish the speech of suicidal persons from that of depressed and control persons with high classification rates. Since the vocal tract length vary from person to person, can the classification rates of suicidal persons be improved through speaker normalization? We approach this problem by warping the frequency axis of the mel-cepstral features. The results show that two different approaches yielded the best results: i) by using the maximum-likelihood approach in a gender-independent database to compute the warping factor for a nonlinear warp and ii) by a transformation of the first three formants in a gender-dependent database to compute the warping factor for a linear warp.