Learning to detect bipolar disorder and borderline personality disorder with language and speech in non-clinical interviews

Conference item

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.

Authors: Wang, B; Wu, Y; Taylor, N; Lyons, T; Liakata, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: International Speech Communication Association
• Host title: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
• Pages: 437-441
• Publication date: 2020-11-16
• Acceptance date: 2020-07-26
• Event title: Annual Conference of the International Speech Communication Association (INTERSPEECH 2020)
• Event website: http://www.interspeech2020.org/
• Event start date: 2020-10-25
• Event end date: 2020-10-29
• DOI: 10.21437/Interspeech.2020-3040
• ISSN: 1990-9772
• Language: English
• Keywords: spoken dialogue; computational paralinguistics; bipolar disorder; borderline personality disorder; FFR; path signature