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Sociability impairment, such as decreased social network size and socialization, is implicated in mental health disorders. To complement the existing self-reports-based assessment of sociability measures, which could be error-prone and burdensome, we propose to estimate an individual’s everyday conversational network from free-living speech recordings obtained with a wearable. Our first contribution is ECoNet, an automatic method to estimate the everyday conversational network using a modular audio processing architecture. Our second contribution is using ECoNet to analyze multiday egocentric audio recordings from 32 individuals representing diverse mental health conditions (healthy controls, depressive disorders, and psychotic disorders). Specifically, we discover that the conversational network size as a sociability measure has a significant correlation with mental health scores. For example, the correlation coefficient between network size and depression severity score was $-0.56$-0.56 ($p<0.01$p<0.01). Audio-based estimation of conversational network size using ECoNet, therefore, could provide a pervasive computing solution to complement existing mental health assessment methods.

ECoNet: Estimating Everyday Conversational Network From Free-Living Audio for Mental Health Applications