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

Author: 

Wang, B
Wu, Y
Taylor, N
Lyons, T
Liakata, M
Nevado-Holgado, A
Saunders, K

Publication Date: 

16 November 2020

Journal: 

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

Last Updated: 

2021-01-07T18:31:42.31+00:00

DOI: 

10.21437/Interspeech.2020-3040

page: 

437-441

abstract: 

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.

Symplectic id: 

1137999

Submitted to ORA: 

Submitted

Publication Type: 

Conference Paper