AVEC 2013: the continuous audio/visual emotion and depression recognition challenge
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
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In Major Depressive Disorder (MDD), neurophysiologic changes can alter motor control [1, 2] and therefore alter speech production by influencing the characteristics of the vocal source, tract, and prosodics. Clinically, many of these characteristics are associated with psychomotor retardation, where a patient shows sluggishness and motor disorder in vocal articulation, affecting coordination across multiple aspects of production [3, 4]. In this paper, we exploit such effects by selecting features that reflect changes in coordination of vocal tract motion associated with MDD. Specifically, we investigate changes in correlation that occur at different time scales across formant frequencies and also across channels of the delta-mel-cepstrum. Both feature domains provide measures of coordination in vocal tract articulation while reducing effects of a slowly-varying linear channel, which can be introduced by time-varying microphone placements. With these two complementary feature sets, using the AVEC 2013 depression dataset, we design a novel Gaussian mixture model (GMM)-based multivariate regression scheme, referred to as Gaussian Staircase Regression, that provides a root-mean-squared-error (RMSE) of 7.42 and a mean-absolute-error (MAE) of 5.75 on the standard Beck depression rating scale. We are currently exploring coordination measures of other aspects of speech production, derived from both audio and video signals.