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

2026-05-01

AI Summary

This study addresses the limitations of traditional speech therapy, which suffers from low assessment efficiency, heavy burdens in developing personalized treatment plans, and a lack of automated support. The authors propose a virtual speech therapist platform that integrates deep learning with a multi-agent large language model (LLM). The system first employs deep learning to automatically analyze speech samples and classify stuttering types, then leverages multiple LLM agents—collaborating through generation, critique, and iterative refinement—to produce individualized therapy plans. Crucially, a dedicated critique agent enforces adherence to clinical evidence-based guidelines, ensuring safety and regulatory compliance. Expert evaluations demonstrate that the system consistently generates high-quality, evidence-supported therapeutic recommendations, significantly reducing clinical workload under physician supervision while enhancing treatment accessibility and efficacy.

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 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 generated 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-the-loop 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. An interactive user interface for the proposed system is available online at: https://vocametrix.com/ai/stuttering-therapy-planning-agent , facilitating real-time stuttering assessment and personalized therapy planning.

Problem

• stuttering therapy
• personalized treatment
• clinician-in-the-loop
• speech impairment
• evidence-based practice

Innovation

• clinician-in-the-loop
• multi-agent LLM reasoning
• stuttering classification
• evidence-based therapy planning
• adaptive AI workflows