title: Critical analysis of the impact of glottal features in the classification of clinical depression in speech.
abstract: The motivation for this work is in an attempt to rectify the current lack of objective tools for clinical analysis of emotional disorders. This study involves the examination of a large breadth of objectively measurable features for use in discriminating depressed speech. Analysis is based on features related to prosodics, the vocal tract, and parameters extracted directly from the glottal waveform. Discrimination of the depressed speech was based on a feature selection strategy utilizing the following combinations of feature domains: prosodic measures alone, prosodic and vocal tract measures, prosodic and glottal measures, and all three domains. The combination of glottal and prosodic features produced better discrimination overall than the combination of prosodic and vocal tract features. Analysis of discriminating feature sets used in the study reflect a clear indication that glottal descriptors are vital components of vocal affect analysis.
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, 210 Technology Circle, Savannah, GA 31407, USA. emoore@gtsav.gatech.edu
authors.forename: Elliot
authors.initials: E
authors.lastname: Moore
authors.forename: Mark A
authors.initials: MA
authors.lastname: Clements
authors.forename: John W
authors.initials: JW
authors.lastname: Peifer
authors.forename: Lydia
authors.initials: L
authors.lastname: Weisser
mesh_terms: D003866:Depressive Disorder / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005931:Glottis / Q000503:physiopathology*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*; D013064:Speech Disorders / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology*; D013068:Speech Production Measurement / Q000379:methods*; D014833:Voice Quality