对比四类语音障碍检测模型，揭示准确率、可控性与可解释性的权衡。
A Comparative Study of Controllability, Explainability, and Performance in Dysfluency Detection Models
- 对比 YOLO-Stutter、FluentNet、UDM、SSDM 四种模型的性能与可解释性。
- UDM 在准确率和临床可解释性上表现最佳，适合实际应用。
- 实验发现 SSDM 无法复现，提示模型可靠性需谨慎评估。
近期语音障碍检测研究提出了多种建模范式，从轻量级目标检测模型（YOLOStutter）到模块化可解释框架（UDM）。尽管基准数据集上的性能持续提升，但临床应用不仅需要高准确率，还需模型具备可控性和可解释性。本文系统比较了四种代表性方法——YOLO-Stutter、FluentNet、UDM 和 SSDM——在性能、可控性与可解释性三个维度的表现。通过多数据集评估及临床专家打分，发现 YOLO-Stutter 和 FluentNet 具有高效简洁优势，但透明度有限；UDM 在准确率与临床可解释性间取得最佳平衡；而 SSDM 虽具潜力，但在本实验中未能完全复现。分析揭示了各方法间的权衡关系，并指明未来临床可用模型的发展方向。同时提供各方法的实现细节与部署建议。
原文摘要 · Abstract (English)
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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