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