findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding
This toolkit addresses the problem of inconsistent implementations and evaluations in syllable-level speech processing for researchers, though it is incremental as it standardizes existing methods rather than introducing new ones.
The authors tackled the fragmentation in syllable-level speech research by introducing findsylls, a language-agnostic toolkit that unifies methods for syllable segmentation and embedding, demonstrating it on English, Spanish, and Kono corpora to support reproducible experiments across diverse languages.
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.