SpeechTherapyAgent: A Clinician-in-the-Loop AI Virtual Speech Therapist for Personalized and Supervised Therapy

This paper introduces Virtual Speech Therapist (VST) 1 , an agentic, AI-powered workflow designed for automated stuttering assessment and personalized therapy planning. VST integrates deep learning-based stuttering classification, and multiagent 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 types. Building on these outputs, VST initiates an agentic reasoning process in which specialized LLM agents autonomously generate, critique, and iteratively refine individualized therapy plans. A dedicated critic agent evaluates all generates therapy plans to ensure clinical safety, methodological soundness, and alignment with peer-reviewed evidence and established professional guidelines. The resulting output is a comprehensive, patient-specific therapy draft intended for clinician review. Incorporating clinician feedback, the system then produces a finalized therapy plan suitable for patient delivery, thereby maintaining a clinician-in-theloop paradigm. Experimental evaluation by expert speech therapists confirms that VST consistently generates high-quality, evidence-based therapy recommendations. These findings demonstrate the system’s potential to augment clinical workflows,reduce clinician burden, and improve therapeutic outcomes for individuals with speech impairments. User Interface: https://vocametrix.com/ai/therapy-planning-agent