Automated assessment of psychiatric disorders using speech: A systematic review

By Daniel M. Low, Kate H Bentley, Satrajit S. Ghosh. Laryngoscope Investigative Otolaryngology, 2019

489 citations recorded by the source corpus.

There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/f6f64f572d2bc5f38526401b1bd346b00b15bccd

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Citations in the corpus

• The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “By monitoring patterns in speech [51], behavior [52], and social media activity [53,54], AI tools can help in identifying the onset of mental health issues and suggest interventions tailored to the individual’s unique situation.”