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Bipolar disorder (BD) and borderline personality disorder (BPD) are twochronic mental health conditions that clinicians find challenging todistinguish based on clinical interviews, due to their overlapping symptoms. Inthis work, we investigate the automatic detection of these two conditions bymodelling both verbal and non-verbal cues in a set of interviews. We propose anew approach of modelling short-term features with visibility-signaturetransform, and compare it with widely used high-level statistical functions. Wedemonstrate the superior performance of our proposed signature-based model.Furthermore, we show the role of different sets of features in characterisingBD and BPD.

Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder