Applications of Speech Analysis in Psychiatry. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Dikaios, Katerina; Rempel, Sheri; Dumpala, Sri Harsha; Oore, Sageev; Kiefte, Michael; Uher, Rudolf
• Is Part Of: Harvard review of psychiatry. Volume 31:Issue 1(2023)
• Page Start: 1
• Page End: 13
• Publication Date: 2023-01
• Subjects: Speech analysis -- objective assessment of psychopathology -- mental disorders -- classifying diagnoses -- automated analysis; Psychiatry -- Periodicals; 616.89005
• DOI: 10.1097/HRP.0000000000000356
• Languages: English
• ISSNs: 1067-3229

Abstract

Abstract : Abstract: The need for objective measurement in psychiatry has stimulated interest in alternative indicators of the presence and severity of illness. Speech may offer a source of information that bridges the subjective and objective in the assessment of mental disorders. We systematically reviewed the literature for articles exploring speech analysis for psychiatric applications. The utility of speech analysis depends on how accurately speech features represent clinical symptoms within and across disorders. We identified four domains of the application of speech analysis in the literature: diagnostic classification, assessment of illness severity, prediction of onset of illness, and prognosis and treatment outcomes. We discuss the findings in each of these domains, with a focus on how types of speech features characterize different aspects of psychopathology. Models that bring together multiple speech features can distinguish speakers with psychiatric disorders from healthy controls with high accuracy. Differentiating between types of mental disorders and symptom dimensions are more complex problems that expose the transdiagnostic nature of speech features. Convergent progress in speech research and computer sciences opens avenues for implementing speech analysis to enhance objectivity of assessment in clinical practice. Application of speech analysis will need to address issues of ethics and equity, including the potential to perpetuate discriminatory bias through models that learn from clinical assessment data. Methods that mitigate bias are available and should play a key role in the implementation of speech analysis.