This paper proposes a clinician-in-the-loop speech therapy agent that combines stuttering classification with multi-agent LLM reasoning to support personalized therapy planning. It is notable for grounding agentic AI in a supervised clinical workflow rather than a purely autonomous setting.
arXiv:2605.01101v1 Announce Type: new 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…