A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models

• URL: http://arxiv.org/abs/2509.00058v1
• Date: Mon, 25 Aug 2025 14:23:09 GMT
• Title: A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models
• Authors: Eric Zhang, Li Wei, Sarah Chen, Michael Wang,
• Abstract summary: We compare four dysfluency modeling approaches: YOLO-Stutter, FluentNet, UDM, and SSDM.<n>YOLO-Stutter and FluentNet provide efficiency and simplicity, but with limited transparency.<n>UDM achieves the best balance of accuracy and clinical interpretability.
• Score: 6.837099592935974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in dysfluency detection have introduced a variety of modeling paradigms, ranging from lightweight object-detection inspired networks (YOLOStutter) to modular interpretable frameworks (UDM). While performance on benchmark datasets continues to improve, clinical adoption requires more than accuracy: models must be controllable and explainable. In this paper, we present a systematic comparative analysis of four representative approaches--YOLO-Stutter, FluentNet, UDM, and SSDM--along three dimensions: performance, controllability, and explainability. Through comprehensive evaluation on multiple datasets and expert clinician assessment, we find that YOLO-Stutter and FluentNet provide efficiency and simplicity, but with limited transparency; UDM achieves the best balance of accuracy and clinical interpretability; and SSDM, while promising, could not be fully reproduced in our experiments. Our analysis highlights the trade-offs among competing approaches and identifies future directions for clinically viable dysfluency modeling. We also provide detailed implementation insights and practical deployment considerations for each approach.

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