Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection
Abstract
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
Keywords
Cite
@article{arxiv.2409.09621,
title = {Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection},
author = {Xuanru Zhou and Cheol Jun Cho and Ayati Sharma and Brittany Morin and David Baquirin and Jet Vonk and Zoe Ezzes and Zachary Miller and Boon Lead Tee and Maria Luisa Gorno Tempini and Jiachen Lian and Gopala Anumanchipalli},
journal= {arXiv preprint arXiv:2409.09621},
year = {2024}
}
Comments
IEEE Spoken Language Technology Workshop 2024