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arXiv · 2509.14304

Deploying UDM Series in Real-Life Stuttered Speech Applications: A Clinical Evaluation Framework

Eric Zhang·Li Wei·Sarah Chen·Michael Wang
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Abstract

Stuttered and dysfluent speech detection systems have traditionally suffered from the trade-off between accuracy and clinical interpretability. While end-to-end deep learning models achieve high performance, their black-box nature limits clinical adoption. This paper looks at the Unconstrained Dysfluency Modeling (UDM) series-the current state-of-the-art framework developed by Berkeley that combines modular architecture, explicit phoneme alignment, and interpretable outputs for real-world clinical deployment. Through extensive experiments involving patients and certified speech-language pathologists (SLPs), we demonstrate that UDM achieves state-of-the-art performance (F1: 0.89+-0.04) while providing clinically meaningful interpretability scores (4.2/5.0). Our deployment study shows 87% clinician acceptance rate and 34% reduction in diagnostic time. The results provide strong evidence that UDM represents a practical pathway toward AI-assisted speech therapy in clinical environments.

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Eric Zhang, Li Wei, Sarah Chen, Michael Wang. 2025-09-17. Deploying UDM Series in Real-Life Stuttered Speech Applications: A Clinical Evaluation Framework. https://arxiv.org/abs/2509.14304

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