From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech

Abstract:Automatic stuttering systems primarily detect dysfluencies after they have occurred, limiting their use for anticipatory intervention. We address this gap by formulating stuttering prediction as continuous causal time-to-event forecasting, where a model estimates the risk of an upcoming stuttering onset every 100 ms using only preceding speech. To support this task, we augment FluencyBank Timestamped for 36 speakers with manually reviewed repetitions, prolongations, and blocks, yielding 2,872 forecasting events. We then model the temporal evolution of frozen Whisper-large-v3 speech representations using a compact causal Conformer with 3.67M trainable parameters. The proposed approach achieves an AUROC of 0.63. Temporal analysis further shows that explicitly modeling preceding speech states outperforms simply increasing the static acoustic window, while a discrete-time survival formulation provides coherent forecasts from 300 ms to 1.5 s using a single model. These results provide initial evidence that stuttering onset is preceded by measurable temporal information in speech and establish a framework for future anticipatory stuttering systems.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2610.19235 [cs.SD]

(or arXiv:2610.19235v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.19235 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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