Title :
Detection of Clinical Depression in Adolescents’ Speech During Family Interactions
Author :
Low, Lu-Shih Alex ; Maddage, Namunu C. ; Lech, Margaret ; Sheeber, Lisa B. ; Allen, Nicholas B.
Author_Institution :
Sch. of Electr. & Comput. Eng., R. Melbourne Inst. of Technol., Melbourne, VIC, Australia
fDate :
3/1/2011 12:00:00 AM
Abstract :
The properties of acoustic speech have previously been investigated as possible cues for depression in adults. However, these studies were restricted to small populations of patients and the speech recordings were made during patients´ clinical interviews or fixed-text reading sessions. Symptoms of depression often first appear during adolescence at a time when the voice is changing, in both males and females, suggesting that specific studies of these phenomena in adolescent populations are warranted. This study investigated acoustic correlates of depression in a large sample of 139 adolescents (68 clinically depressed and 71 controls). Speech recordings were made during naturalistic interactions between adolescents and their parents. Prosodic, cepstral, spectral, and glottal features, as well as features derived from the Teager energy operator (TEO), were tested within a binary classification framework. Strong gender differences in classification accuracy were observed. The TEO-based features clearly outperformed all other features and feature combinations, providing classification accuracy ranging between 81%-87% for males and 72%-79% for females. Close, but slightly less accurate, results were obtained by combining glottal features with prosodic and spectral features (67%-69% for males and 70%-75% for females). These findings indicate the importance of nonlinear mechanisms associated with the glottal flow formation as cues for clinical depression.
Keywords :
medical disorders; medical signal processing; patient diagnosis; signal classification; speech; speech processing; Teager energy operator; adolescence; cepstral feature; clinical depression detection; family interactions; fixed-text reading session; glottal feature; glottal flow formation; patient clinical interviews; prosodic feature; signal classification; spectral feature; speech recordings; Acoustics; Correlation; Feature extraction; Frequency measurement; Harmonic analysis; Speech; Timing; Acoustic features; adolescents; clinical depression classification; naturalistic speech; Adolescent; Adolescent Psychology; Depression; Diagnosis, Computer-Assisted; Family Relations; Female; Humans; Male; Pattern Recognition, Automated; Speech; Speech Acoustics;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2010.2091640