YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection

Xuanru Zhou, Anshul Kashyap, Steve Li, Ayati Sharma, Brittany Morin +7 more
8/27/2024
eess.AScs.AIcs.CL

Official code: rorizzz/YOLO-Stutter (21 stars, MIT, PyTorch, last commit 2025-03-04).

Abstract

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

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Code Implementations(1)

rorizzz/YOLO-StutterOfficial100%
PyTorchMIT

YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection

213Cython, PythonJun 6, 20241 years agoMIT
deep-learningdysfluency-detectionpytorchspeech-modelling

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Cite this paper

@article{zhou2024yolostutter,
  title  = {YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection},
  author = {Xuanru Zhou and Anshul Kashyap and Steve Li and Ayati Sharma and Brittany Morin and David Baquirin and Jet Vonk and Zoe Ezzes and Zachary Miller and Maria Luisa Gorno Tempini and Jiachen Lian and Gopala Krishna Anumanchipalli},
  year   = {2024},
  eprint = {2408.15297},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.15297v3}
}

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