StutterCut: Uncertainty-Guided Normalised Cut for Dysfluency Segmentation

• URL: http://arxiv.org/abs/2508.02255v1
• Date: Mon, 04 Aug 2025 10:02:06 GMT
• Title: StutterCut: Uncertainty-Guided Normalised Cut for Dysfluency Segmentation
• Authors: Suhita Ghosh, Melanie Jouaiti, Jan-Ole Perschewski, Sebastian Stober,
• Abstract summary: We introduce StutterCut, a semi-supervised framework that formulates dysfluency segmentation as a graph problem partitioning problem.<n>We refine the connections between nodes using a pseudo-oracle classifier trained on weak (utterance-level) labels.<n>We extend the weakly labelled FluencyBank dataset by incorporating frame-level dysfluency boundaries for four dysfluency types.
• Score: 0.0874967598360817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting and segmenting dysfluencies is crucial for effective speech therapy and real-time feedback. However, most methods only classify dysfluencies at the utterance level. We introduce StutterCut, a semi-supervised framework that formulates dysfluency segmentation as a graph partitioning problem, where speech embeddings from overlapping windows are represented as graph nodes. We refine the connections between nodes using a pseudo-oracle classifier trained on weak (utterance-level) labels, with its influence controlled by an uncertainty measure from Monte Carlo dropout. Additionally, we extend the weakly labelled FluencyBank dataset by incorporating frame-level dysfluency boundaries for four dysfluency types. This provides a more realistic benchmark compared to synthetic datasets. Experiments on real and synthetic datasets show that StutterCut outperforms existing methods, achieving higher F1 scores and more precise stuttering onset detection.

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