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Modeling spectral variability for the classification of depressed speech
Quantifying how the spectral content of speech relates to changes in mental state may be crucial in building an objective speech-based depression classification system with clinical utility. This paper investigates the hypothesis that important depression based information can be captured within the covariance structure of a Gaussian Mixture Model (GMM) of recorded speech. Significant negative correlations found between a speaker's average weighted variance -a GMMbased indicator of speakerdoi:10.21437/interspeech.2013-242 fatcat:ijx3qpdiondzjobb253fkfybry