Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy
arXiv:2605.01101v2 Announce Type: replace-cross Abstract: This paper develops Virtual Speech Therapist (VST), an intelligent agent-based platform that streamlines stuttering assessment and delivers customized therapy planning through automated and adaptive AI-driven workflows. VST integrates state-of-the-art deep learning-based stuttering classification, and multi-agent large language model (LLM) reasoning to support evidence-based clinical decision-making. The VST begins with the acquisition and feature extraction of patient speech samples, followed by robust classification of stuttering type
The rapid advancement in deep learning for speech classification and multi-agent large language models enables the development of sophisticated AI-driven therapeutic agents like VST.
This development represents a significant step towards scalable, personalized, and efficient AI-driven healthcare, potentially overcoming geographical and resource limitations in specialized therapy.
Therapy for conditions like stuttering can become more accessible and tailored, shifting from purely human-led to hybrid models integrating advanced AI for assessment and treatment planning.
- · AI healthcare platforms
- · Patients with speech disorders
- · LLM developers
- · Specialized therapy clinics (early adopters)
- · Traditional therapy models (resistant to AI integration)
- · Therapists relying solely on manual assessment
AI models will begin to deliver personalized healthcare services directly to patients, particularly in chronic conditions.
The successful deployment of VST may lead to similar AI agent development across various medical specialties, accelerating the adoption of clinician-in-the-loop AI.
This could contribute to the development of regulatory frameworks specifically for AI agents in healthcare, defining standards for safety, efficacy, and ethical deployment.
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