统一多语言音节分割与嵌入工具，支持跨语言可复现实验
findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding
- 构建统一接口的模块化工具，整合经典与端到端音节分割方法
- 在英语、西班牙语及科诺语数据上验证，支持高低资源语言研究
- 适合语音建模、无监督词发现等任务的研究者使用
音节级单元为语音建模和无监督词发现提供了紧凑且具语言学意义的表示，但音节切分研究分散于不同实现、数据集和评估协议中。我们提出 findsylls，一个模块化、语言无关的工具包，统一了经典音节检测器与端到端音节分割器，提供音节分割、嵌入提取和多粒度评估的统一接口。该工具包实现了并标准化了多种常用方法（如 Sylber、VG-HuBERT），支持组件灵活重组，便于对表示、算法和标记率进行受控比较。我们在英语和西班牙语语料库以及新标注的科诺语（一种低资源中曼德语）数据上验证 findsylls，展示单一框架如何在高资源与低资源场景下支持可复现的音节级实验。
原文摘要 · Abstract (English)
Syllable-level units offer compact and linguistically meaningful representations for spoken language modeling and unsupervised word discovery, but research on syllabification remains fragmented across disparate implementations, datasets, and evaluation protocols. We introduce findsylls, a modular, language-agnostic toolkit that unifies classical syllable detectors and end-to-end syllabifiers under a common interface for syllable segmentation, embedding extraction, and multi-granular evaluation. The toolkit implements and standardizes widely used methods (e.g., Sylber, VG-HuBERT) and allows their components to be recombined, enabling controlled comparisons of representations, algorithms, and token rates. We demonstrate findsylls on English and Spanish corpora and on new hand-annotated data from Kono, an underdocumented Central Mande language, illustrating how a single framework can support reproducible syllable-level experiments across both high-resource and under-resourced settings.
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