  Bipolar disorder (BD) and borderline personality disorder (BPD) are both
chronic psychiatric disorders. However, their overlapping symptoms and common
comorbidity make it challenging for the clinicians to distinguish the two
conditions on the basis of a clinical interview. In this work, we first present
a new multi-modal dataset containing interviews involving individuals with BD
or BPD being interviewed about a non-clinical topic . We investigate the
automatic detection of the two conditions, and demonstrate a good linear
classifier that can be learnt using a down-selected set of features from the
different aspects of the interviews and a novel approach of summarising these
features. Finally, we find that different sets of features characterise BD and
BPD, thus providing insights into the difference between the automatic
screening of the two conditions.
