title: Multichannel weighted speech classification system for prediction of major depression in adolescents.
abstract: Early identification of adolescents at high imminent risk for clinical depression could significantly reduce the burden of the disease. This study demonstrated that acoustic speech analysis and classification can be used to determine early signs of major depression in adolescents, up to two years before they meet clinical diagnostic criteria for the full-blown disorder. Individual contributions of four different types of acoustic parameters [prosodic, glottal, Teager's energy operator (TEO), and spectral] to depression-related changes of speech characteristics were examined. A new computational methodology for the early prediction of depression in adolescents was developed and tested. The novel aspect of this methodology is in the introduction of multichannel classification with a weighted decision procedure. It was observed that single-channel classification was effective in predicting depression with a desirable specificity-to-sensitivity ratio and accuracy higher than chance level only when using glottal or prosodic features. The best prediction performance was achieved with the new multichannel method, which used four features (prosodic, glottal, TEO, and spectral). In the case of the person-based approach with two sets of weights, the new multichannel method provided a high accuracy level of 73% and the sensitivity-to-specificity ratio of 79%/67% for predicting future depression.
authors.affiliation: School of Electrical and Computer Engineering, Royal Melbourne Institute of Technology, Melbourne, Victoria, Australia. k.ooi@student.rmit.edu.au
authors.forename: Kuan Ee Brian
authors.initials: KE
authors.lastname: Ooi
authors.forename: Margaret
authors.initials: M
authors.lastname: Lech
authors.forename: Nicholas B
authors.initials: NB
authors.lastname: Allen
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D002648:Child; D016208:Databases, Factual; D003865:Major Depressive Disorder / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted; D013060:Speech / Q000145:classification*; D013061:Speech Acoustics