Speech analysis can help gauge the prognosis, severity, and onset of mental illness

summary: Speech pattern analysis can aid in the accurate diagnosis of depression and psychosis, measure the severity of symptoms, and predict the onset of mental health conditions.

Source: Walters Kluwer Health

Objective measurement of mental disorders has long proven difficult. However, there is ample evidence that analysis of speech patterns can accurately diagnose depression and psychosis, measure their severity, and predict their onset, according to a literature review that appeared in the January/February issue of the journal Psychology. Harvard Review of Psychiatry.

The review examined existing published literature related to the use of speech pattern analysis for the management of psychiatric disorders and identified four main areas of application: diagnostic classification, assessment of severity, and prediction of onset, as well as diagnostic and treatment outcomes.

“Models that combine multiple speech features can accurately distinguish psychiatrically disturbed speakers from healthy controls,” write Rudolph O’Hare, MD, MD, Department of Psychiatry at Dalhousie University, Nova Scotia Health, and colleagues, Katrina Decaus, MSc, Sherry Rempel. Alia” MA, Sri Harsha Dumpala, MA, Sageev Oore, PhD, and Michael Kiefte, PhD, in the January/February issue of Harvard Review of Psychiatry.

Automated analysis is more promising than subjective measures such as interviews or questionnaires

Characteristics of mental illness are often presented through speech and language, and the clinical-psychological assessment must take into account patterns in the patient’s speech – such as pacing, coherence, and content.

Advances in natural language processing, speech recognition, and computer science have confirmed the fact that the use of speech analysis as an objective clinical measure of mental illness is possible.

The research team reviewed hundreds of articles, papers, and reports of individuals with a mental disorder who discussed aspects of their speech. Case studies and studies of patients with neurological disorders were excluded from the review. It included articles that analyzed participants’ speech transcripts.

Most of the studies included in the review discussing the use of speech analysis in diagnosis concerned patients with major depression, whose speech was often slow, full of pauses, negative in content, and lacking in energy. In these, diagnostic accuracy was high, over 80% in one study.

Instrumental analysis is also effective in predicting the onset of mental illness, particularly in high-risk populations. Several studies that looked at speech semantics, including consistency and complexity, predicted the onset of psychosis within two to two and a half years with almost 100% accuracy. However, the literature on the impact of speech analysis on diagnostic and treatment outcomes is limited and more research is needed.

It shows two heads and speech bubbles
Characteristics of mental illness are often presented through speech and language, and the clinical-psychological assessment must take into account patterns in the patient’s speech – such as pacing, coherence, and content. The image is in the public domain

Importantly, the use of speech pattern analysis in assessing suicide risk appears to have great potential. One recent study showed that measuring variables such as erratic frequency, hesitation, and stress identified patients with suicidal thoughts against healthy patients 73% of the time.

Speech variation, other issues remain

Many factors, such as the effects of medications, as well as demographic and cultural characteristics—language, gender, and sexuality, among others—can cause variability in speech patterns and make it difficult to integrate speech into an objective assessment of disease and outcome.

In addition, the authors suggest that any further research should consider disease states across time, as most of the studies examined here looked at current patients rather than examining whether similar patterns persist over the long term between symptoms.

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author: Josh DiStefano
Source: Walters Kluwer Health
Contact: Josh DiStefano – Walters Kluwer Health
picture: The image is in the public domain

Original search: Closed access.
Applications of speech analysis in psychiatryBy Rudolf Oher et al. Harvard Review of Psychiatry


See also

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Applications of speech analysis in psychiatry

The need for objective measurement in psychiatry has stimulated interest in surrogate indicators of disease presence and severity. Speech may provide a source of information linking subjectivity and objectivity in the assessment of mental disorders.

We systematically reviewed the literature for articles exploring speech analysis for psychiatry applications. The usefulness of speech analysis depends on how accurately speech features represent clinical symptoms within and across disorders.

We identified four areas of application of speech analysis in the literature: diagnostic classification, assessment of disease severity, prediction of disease onset, prognosis and treatment outcome.

We discuss findings in each of these areas, focusing on how types of speech traits characterize different aspects of psychopathology. Models that combine multiple speech features can distinguish speakers with psychiatric disorders from healthy controls with high accuracy.

Differentiating between types of psychiatric disorders and symptomatic dimensions are more complex issues that reveal the trans-diagnostic nature of speech features. Converging advances in speech research and computer science open avenues for implementing speech analysis to enhance the objectivity of assessment in clinical practice.

The application of speech analysis will need to address issues of ethics and fairness, including the potential for perpetuating discriminatory bias through models that learn from clinical assessment data.

Methods that mitigate bias are available and should play a major role in implementing speech analysis.

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