Automated assessment of psychiatric disorders using speech: A systematic review

Laryngoscope Investigative Otolaryngology
Daniel M LowSatrajit S Ghosh

Abstract

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. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). For each study, we describe sample size, clinical evaluation method, speech-eliciting tasks, machine learning methodology, performance, and other relevant findings. 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia,...Continue Reading

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Citations

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Methods Mentioned

BETA
feature extraction
biopsy

Software Mentioned

Google Scholar
Praat
ELAN
Google Cloud Speech
Google Cloud Speech ‐ to ‐ Text
Singularity
MultiSense
MONARCA
rev
Mozilla DeepSpeech

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