overview: Voice pattern analysis can help accurately diagnose depression and psychosis, measure symptom severity, and predict the onset of mental health conditions.
sauce: Walters Kluwer Health
Objective measurement of mental disorders has long proven difficult. Still, there is ample evidence that analysis of speech patterns can accurately diagnose depression and psychosis, measure their severity, and predict their onset. Harvard Review of Psychiatry.
This review surveys the currently published literature related to the use of speech pattern analysis to manage psychiatric disorders and identifies four important application areas: diagnostic classification, severity assessment, onset prediction, prognosis and treatment outcome. has been identified.
“A model that combines multiple speech features can distinguish between psychopathic speakers and healthy controls with high accuracy,” says Rudolf Uher, Ph.D., MD, and colleagues of the Dalhousie School of Psychiatry and Nova Scotia Health Department. Katerina Dikaios MSc Sheri Rempel writes: MSc, Sri Harsha Dumpala, MSc, Sageev Oore, PhD, Michael Kiefte, PhD, January/February issue Harvard Review of Psychiatry.
Automated analysis holds promise over subjective measurements such as interviews and surveys
Mental illness is often characterized by speech and language. A psychiatric clinical assessment should consider the patient’s speech patterns, including speed, coherence, and content.
Advances in natural language processing, speech recognition, and computer science underscore the fact that clinical measurement of mental illness is possible using speech analysis as an objective tool.
The research team reviewed hundreds of articles, papers, and reports discussing aspects of speech in individuals with mental disorders. Case studies and studies in patients with neuropathy were excluded from the review. They included articles analyzing transcripts of participants’ speeches.
Most of the studies included in reviews discussing the use of voice analysis in diagnosis involved patients with major depression, whose speech was often slow, paused, negative in content, and lacked energy. I’m here. These diagnostic accuracies were high, over 80% in one study.
Automated analysis is also effective in predicting the development of psychiatric disorders, especially in high-risk populations. Several studies examining speech semantics, such as coherence and complexity, predicted the onset of psychosis with 100% accuracy at 2 to 2.5 years. However, the literature on the impact of voice analysis on prognosis and treatment outcome is limited and further research is needed.
Importantly, the use of speech pattern analysis to assess suicide risk appears to have great potential. A recent study showed that measuring variables such as irregularity frequency, hesitation, and jitter identified suicidal patients 73% of the time compared to healthy patients.
Speech distribution and other issues remain
A variety of factors, including the effects of medications and demographic and cultural attributes such as language, gender, and gender, lead to variability in speech patterns and the inability of speech to be incorporated into objective disease and outcome assessments. can make it difficult.
In addition, the authors suggest that future studies should examine disease status over time, as most of the studies investigated here looked at currently ill patients, rather than whether similar patterns endured long-term between symptoms. This suggests that it should be considered over time.
About this speech and mental health research news
author: Josh Destefano
sauce: Walters Kluwer Health
contact: Josh DeStefano – Wolters Kluwer Health
image: image is public domain
Original research: closed access.
“Applications of Speech Analysis in Psychiatry” by Rudolf Uher et al. Harvard Review of Psychiatry
overview
See also
Applications of Speech Analysis in Psychiatry
The need for objective measurements in psychiatry has stimulated interest in surrogate indicators of disease presence and severity. Speech may provide a source of information that bridges the subjective and the objective in evaluating mental disorders.
We systematically reviewed the literature for articles investigating speech analysis for psychiatric applications. The usefulness of voice analysis depends on how accurately voice features represent clinical manifestations within and across disorders.
We have identified four domains of application of speech analysis in the literature: diagnostic classification, disease severity assessment, disease onset prediction, prognosis and treatment outcome.
We discuss findings in each of these domains and focus on how types of speech function characterize different aspects of psychopathology. Models combining multiple speech features can distinguish between psychotic speakers and healthy controls with high accuracy.
Distinguishing between types of psychiatric disorders and dimensions of symptoms is a more complex problem that reveals the transdiagnostic nature of voice function. Converging advances in speech research and computer science pave the way for implementing speech analysis to increase the objectivity of assessments in clinical practice.
Applications of speech analytics must address ethical and fairness issues, including the potential for perpetuating discriminatory biases through models learning from clinical assessment data.
Methods to mitigate bias are available and should play an important role in speech analysis implementations.
