arXiv:2509.00058v1 Announce Type: new
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.
Expert Commentary: Analyzing Dysfluency Detection Approaches
As a pioneer in the field of dysfluency detection, this paper delves into the complexities of modeling paradigms and the challenges of clinical adoption. The multi-disciplinary nature of this research is evident in the intersection of machine learning, clinical psychology, and linguistics. Each approach, from YOLO-Stutter to SSDM, brings unique strengths and weaknesses to the table.
Performance Evaluation
- YOLO-Stutter and FluentNet offer efficiency and simplicity in their modeling paradigms, making them attractive choices for real-time applications.
- UDM stands out for achieving a balance between accuracy and clinical interpretability, essential for practical clinical deployment.
- SSDM shows promise but faces challenges in reproducibility, pointing towards the need for more robust methodologies.
Controllability and Explainability
One of the critical aspects of dysfluency detection models is their controllability and explainability. Models must not only provide accurate results but also be understandable to clinicians for effective patient care. UDM emerges as a frontrunner in this aspect, bridging the gap between technical complexity and clinical utility.
Future Directions
As the field of dysfluency modeling evolves, the insights from this comparative analysis pave the way for future research directions. The trade-offs among efficiency, interpretability, and reproducibility are crucial considerations for researchers and practitioners alike. The paper’s detailed implementation insights and practical deployment considerations offer valuable guidance for the development and deployment of clinically viable dysfluency detection models.
Overall, this study underscores the importance of not only advancing the performance of dysfluency detection models but also enhancing their controllability and explainability for seamless integration into clinical practice.