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dc.contributor.authorSaunders, Kate E.A.
dc.date.accessioned2021-04-14T21:38:45Z
dc.date.available2021-04-14T21:38:45Z
dc.date.issued2021-02
dc.identifier.citationBo Wang , Yue Wu, Nemanja Vaci , Maria Liakata , Terry Lyons, Kate E A Saunders. Modelling paralinguistic properties in conversational speech to detect bipolar disorder and borderline personality disorder. arXiv:2102.09607en
dc.identifier.urihttps://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/780
dc.description.abstractBipolar 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.en
dc.description.sponsorshipSupported by the NIHRen
dc.description.urihttps://arXiv:2102.09607v1en
dc.language.isoenen
dc.titleModelling paralinguistic properties in conversational speech to detect bipolar disorder and borderline personality disorderen
dc.typePreprinten


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