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

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

arXiv · 2408.15297

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

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

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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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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. 2024-08-27. YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection. https://arxiv.org/abs/2408.15297

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