Speech & Language Markers in Psychiatry - The NLP Revolution-View Presentation

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Video Summary

In this presentation, Manu Sharma discusses the potential of using speech and language as biomarkers in psychiatry, focusing on diagnostics and therapy through AI-driven methods. Historically, language has been vital in psychiatric assessments, like identifying speech patterns in disorders such as depression or schizophrenia. Sharma outlines the evolution from manual coding of speech samples to modern AI models, highlighting the use of deep learning systems like GPT and BERT to enhance context understanding in language processing.<br /><br />He reviews studies showcasing speech analysis applications, like predicting depression severity through vocal markers or identifying risk of psychosis via social media analysis. The session explores how AI could aid clinicians by automating symptom measurement, monitoring treatment responses, and even enhancing therapy through feedback mechanisms. Despite the promise, Sharma notes critical challenges, such as cultural and contextual variability, privacy concerns, the need for diverse datasets, and the complexity of speech, urging careful integration of AI in clinical settings. Concluding, he emphasizes AI's role in supplementing, not replacing, human clinical judgment, advocating for scalable, objective measures in psychiatric care.

Keywords

speech biomarkers

psychiatry AI

language processing

deep learning

depression diagnosis

psychosis risk

AI in therapy

privacy concerns

clinical AI integration