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Assessing Depression Severity From Speech: The Role of Clinical Intuition and Artificial Intelligence

dc.contributor.authorLangley, Ross
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Scienceen_US
dc.contributor.departmentDepartment of Psychiatryen_US
dc.contributor.ethics-approvalReceiveden_US
dc.contributor.external-examinerDr. Lena Palaniyappanen_US
dc.contributor.graduate-coordinatorDr. Sherry Stewarten_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Sageev Ooreen_US
dc.contributor.thesis-readerDr. Abraham Nunesen_US
dc.contributor.thesis-supervisorDr. Rudolf Uheren_US
dc.date.accessioned2023-07-20T17:41:17Z
dc.date.available2023-07-20T17:41:17Z
dc.date.defence2023-06-15
dc.date.issued2023-07-20
dc.description.abstractAutomated speech analysis methods are used to estimate depression severity. However, it is unclear how these compare to the intuition of expert clinicians using the same information from speech. We distributed 1-minute speech recordings from 72 participants to 12 clinicians, who estimated depression severity after listening to speech recordings. We trained acoustic and text-based AI models to estimate depression severity in the same samples. Clinicians had a higher agreement to MADRS scores (ICC= 0.47, 95%) than the acoustic-based (ICC = 0.35), text-based (ICC = 0.29), and combined acoustic and text (ICC = 0.33) AI model estimations. However, clinicians had larger errors (RMSE = 10.98) than the text-based (RMSE = 10.02), acoustic-based (RMSE = 10.69), and combined (RMSE = 9.71) AI models. Bias analysis showed clinician gender-based differences in depression estimation. These findings provide the first direct comparison of clinical intuition and AI estimation of depression severity from speech.en_US
dc.identifier.urihttp://hdl.handle.net/10222/82718
dc.language.isoenen_US
dc.subjectDepressionen_US
dc.subjectSpeechen_US
dc.titleAssessing Depression Severity From Speech: The Role of Clinical Intuition and Artificial Intelligenceen_US
dc.typeThesisen_US

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