Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection
<|reference_start|>Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection: Current de-facto dysfluency modeling methods utilize template matching algorithms which are not generalizable to out-of-domain real-world dysfluencies across languages, and are not scalable with increasing amounts of training data. To handle these problems, we propose Stutter-Solver: an end-to-end framework that detects dysfluency with accurate type and time transcription, inspired by the YOLO object detection algorithm. Stutter-Solver can handle co-dysfluencies and is a natural multi-lingual dysfluency detector. To leverage scalability and boost performance, we also introduce three novel dysfluency corpora: VCTK-Pro, VCTK-Art, and AISHELL3-Pro, simulating natural spoken dysfluencies including repetition, block, missing, replacement, and prolongation through articulatory-encodec and TTS-based methods. Our approach achieves state-of-the-art performance on all available dysfluency corpora. Code and datasets are open-sourced at https://github.com/eureka235/Stutter-Solver<|reference_end|>
@article{zhou2024stutter-solver:,
    title={Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection},
    author={Xuanru Zhou, Cheol Jun Cho, Ayati Sharma, Brittany Morin, David
  Baquirin, Jet Vonk, Zoe Ezzes, Zachary Miller, Boon Lead Tee, Maria Luisa
  Gorno Tempini, Jiachen Lian, Gopala Anumanchipalli},
    journal={arXiv preprint arXiv:2409.09621},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09621},
    primaryClass={eess.AS cs.AI cs.SD}
}