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.

翻译：本文开发了虚拟言语治疗师（VST），一个基于智能体的平台，通过自动化与自适应的人工智能驱动流程，简化口吃评估并提供定制化治疗方案。VST集成基于深度学习的先进口吃分类技术以及多智能体大语言模型（LLM）推理，支持循证临床决策。VST首先进行患者语音样本的采集与特征提取，随后对口吃类型进行稳健分类。基于这些输出，VST启动智能体推理过程，由专门的LLM智能体自主生成、评估并迭代优化个性化治疗方案。一个专门的评估智能体对所有生成的治疗方案进行审查，确保其临床安全性、方法学合理性，并与同行评审证据及既定专业指南保持一致。最终输出一份全面的患者特定治疗方案草案，供临床医师审阅。系统结合临床医师反馈后，生成适用于患者实施的最终治疗方案，从而维持临床专家参与式（clinician-in-the-loop）范式。由言语治疗专家进行的实验评估证实，VST能持续生成高质量、基于循证的治疗建议。这些发现表明该系统具有增强临床工作流程、减轻临床医师负担、并改善言语障碍患者治疗效果的潜力。本系统的交互式用户界面可在线访问：https://vocametrix.com/ai/stuttering-therapy-planning-agent，便于进行实时口吃评估与个性化治疗方案规划。