# findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding

arXiv: https://arxiv.org/abs/2603.26292 · HTML: https://mlsanity.com/paper/2603.26292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019)
- [Word Discovery in Visually Grounded, Self-Supervised Speech Models](https://mlsanity.com/paper/2203.15081/) (2022)
- [SyllableLM: Learning Coarse Semantic Units for Speech Language Models](https://mlsanity.com/paper/2410.04029/) (2024)
- [Sylber: Syllabic Embedding Representation of Speech from Raw Audio](https://mlsanity.com/paper/2410.07168/) (2024)
- [SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERT](https://mlsanity.com/paper/2310.10803/) (2023)
- [Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model](https://mlsanity.com/paper/2305.11435/) (2023)
- [Unsupervised lexicon learning from speech is limited by representations rather than clustering](https://mlsanity.com/paper/2510.09225/) (2025)
- [Scaling Spoken Language Models with Syllabic Speech Tokenization](https://mlsanity.com/paper/2509.26634/) (2025)
- [Towards Unsupervised Speech Recognition at the Syllable-Level](https://mlsanity.com/paper/2510.03639/) (2025)

## Abstract (arXiv)

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.