用目标检测思路实现多语言口吃识别，精度高且可扩展。
Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection
- 借鉴YOLO思想，端到端检测口吃类型和时间戳。
- 在多个数据集上达到当前最优性能，支持共现口吃检测。
- 适合语音识别、言语康复领域研究者使用。
现有口吃建模方法依赖模板匹配，难以泛化到跨语言的真实口吃场景，且无法随训练数据增长而扩展。为此，我们提出Stutter-Solver：一个受YOLO目标检测算法启发的端到端框架，可精准识别口吃类型与发生时间。该框架能处理多种口吃共现情况，天然支持多语言。为提升可扩展性并增强性能，我们构建了三个新型口吃语料库：VCTK-Pro、VCTK-Art 和 AISHELL3-Pro，通过语音合成与发音编码方法模拟自然口语中的重复、阻塞、缺失、替换和延长等口吃现象。实验表明，该方法在所有可用口吃语料库上均达到最先进水平。代码与数据集已开源。
原文摘要 · Abstract (English)
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
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