YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection
<|reference_start|>YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection: Dysfluent speech detection is the bottleneck for disordered speech analysis and spoken language learning. Current state-of-the-art models are governed by rule-based systems which lack efficiency and robustness, and are sensitive to template design. In this paper, we propose YOLO-Stutter: a first end-to-end method that detects dysfluencies in a time-accurate manner. YOLO-Stutter takes imperfect speech-text alignment as input, followed by a spatial feature aggregator, and a temporal dependency extractor to perform region-wise boundary and class predictions. We also introduce two dysfluency corpus, VCTK-Stutter and VCTK-TTS, that simulate natural spoken dysfluencies including repetition, block, missing, replacement, and prolongation. Our end-to-end method achieves state-of-the-art performance with a minimum number of trainable parameters for on both simulated data and real aphasia speech. Code and datasets are open-sourced at https://github.com/rorizzz/YOLO-Stutter<|reference_end|>
@article{zhou2024yolo-stutter:,
    title={YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection},
    author={Xuanru Zhou, Anshul Kashyap, Steve Li, Ayati Sharma, Brittany Morin,
  David Baquirin, Jet Vonk, Zoe Ezzes, Zachary Miller, Maria Luisa Gorno
  Tempini, Jiachen Lian, Gopala Krishna Anumanchipalli},
    journal={arXiv preprint arXiv:2408.15297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15297},
    primaryClass={eess.AS cs.AI cs.CL}
}