详细信息
- 来源 站点
- ArXiv CS.CL
- 作者
- Shakeel Sheikh, Patrick Marmaroli, MD Sahidullah, Slim Ouni, Fabrice Hirsch, Goncalo Leal, Bjorn W Schuller
- 文章类型
- NEWS
- 语言
- en
- 发布日期
- 2026-06-16
摘要
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 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.