DocumentCode :
1079410
Title :
Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk
Author :
Ozdas, Asli ; Shiavi, Richard G. ; Silverman, Stephen E. ; Silverman, Marilyn K. ; Wilkes, D. Mitchell
Author_Institution :
Dept. of Biomed. Eng., Vanderbilt Univ., Nashville, TN, USA
Volume :
51
Issue :
9
fYear :
2004
Firstpage :
1530
Lastpage :
1540
Abstract :
Among the many clinical decisions that psychiatrists must make, assessment of a patient´s 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 patient´s 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.
Keywords :
acoustic signal processing; maximum likelihood estimation; medical signal processing; signal classification; speech; speech processing; a posteriori methods; depression; excitation-based speech parameters; glottal flow spectrum; major depressed patient; maximum likelihood classifier; near-term suicidal risk; nonsuicidal patient; patient voice; phonation; statistical analysis; vocal jitter; Biomedical measurements; Demography; History; Instruments; Jitter; Psychiatry; Psychology; Size measurement; Speech; Statistical analysis; Algorithms; Depressive Disorder, Major; Diagnosis, Computer-Assisted; Glottis; Humans; Male; Reproducibility of Results; Risk Assessment; Risk Factors; Sensitivity and Specificity; Sound Spectrography; Suicide; Voice Disorders; Voice Quality;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2004.827544
Filename :
1325813
Link To Document :
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