Voice pattern analysis helps diagnose and manage mental disorders

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 PsychiatryThis journal is featured in Wolters Kluwer’s Lippincott Portfolio.

This review surveys the currently published literature related to the use of speech pattern analysis to manage psychiatric disorders and focuses on 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 colleague of the Department of Psychiatry and Nova Scotia Health, Dalhousie University. 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

The hallmarks of psychiatric illness are often presented through speech and language, and psychiatric clinical assessments should consider the patient’s pattern of speech, including speed, coherence, and content. Advances in natural language processing, speech recognition, and computer science highlight 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 drug influence, language, gender, and demographic and cultural attributes such as gender, lead to variability in speech patterns and make it difficult to incorporate speech into objective assessments of illness and outcome. increase. In addition, the authors note that most of the studies reviewed here looked at currently ill patients rather than whether similar patterns persisted long-term between symptoms, so future studies should be considered over time. This suggests that it is necessary to consider the disease state together with