[2603.26292] findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding

Computer Science > Computation and Language arXiv:2603.26292 (cs) [Submitted on 27 Mar 2026] Title:findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding Authors:Héctor Javier Vázquez Martínez View a PDF of the paper titled findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding, by H\'ector Javier V\'azquez Mart\'inez View PDF HTML (experimental) Abstract: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. Comments: Subjects: Computation and Language (cs.CL); Artificial In...