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

• URL: http://arxiv.org/abs/2603.26292v1
• Date: Fri, 27 Mar 2026 11:03:08 GMT
• Title: findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding
• Abstract summary: findsylls is a language-agnostic toolkit that unifies classical syllable detectors and end-to-end syllabifiers.<n>It implements and standardizes widely used methods (e.g., Sylber, VG-HuBERT) and allows their components to be recombined.<n>We demonstrate findsylls on English and Spanish corpora and on new hand-annotated data from Kono, an underdocumented Central Mande language.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval [53.79335341504602]
  INSPIRE is the first benchmark for instruction-aware speech retrieval.<n>We evaluate four retrieval paradigms: large audio-language models, cascaded pipelines, self-supervised speech models, and contrastive audio-language models.
  arXiv  Detail & Related papers  (2026-08-17T07:35:18Z)
• What Language is This? Ask Your Tokenizer [32.28976119949841]
  Language Identification (LID) is an important component of many multilingual natural language processing pipelines.<n>We introduce UniLID, a simple and efficient LID method based on the UnigramLM tokenization algorithm.<n>Our formulation is data- and compute-efficient, supports incremental addition of new languages without retraining existing models.
  arXiv  Detail & Related papers  (2026-02-19T18:58:39Z)
• A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching [60.51839859852572]
  We propose to resolve the text into multi concepts for multilingual semantic matching to liberate the model from the reliance on NER models. We conduct comprehensive experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
  arXiv  Detail & Related papers  (2024-03-05T13:55:16Z)
• Design and Implementation of a Tool for Extracting Uzbek Syllables [0.0]
  Syllabification is a versatile linguistic tool with applications in linguistic research, language technology, education, and various fields. We present a comprehensive approach to syllabification for the Uzbek language, including rule-based techniques and machine learning algorithms. The results of our experiments show that both approaches achieved a high level of accuracy, exceeding 99%.
  arXiv  Detail & Related papers  (2023-12-25T17:46:58Z)
• Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation [1.2617078020344619]
  Syllables provide shorter sequences than characters, require less-specialised extracting rules than morphemes, and their segmentation is not impacted by the corpus size. We first explore the potential of syllables for open-vocabulary language modelling in 21 languages. We use rule-based syllabification methods for six languages and address the rest with hyphenation, which works as a syllabification proxy.
  arXiv  Detail & Related papers  (2022-10-05T18:55:52Z)
• BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing [55.058258437125524]
  We introduce BenchCLAMP, a Benchmark to evaluate Constrained LAnguage Model Parsing. We benchmark eight language models, including two GPT-3 variants available only through an API. Our experiments show that encoder-decoder pretrained language models can achieve similar performance or surpass state-of-the-art methods for syntactic and semantic parsing when the model output is constrained to be valid.
  arXiv  Detail & Related papers  (2022-06-21T18:34:11Z)
• Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding [90.87454350016121]
  We develop novel code-switching schemes to generate hard negative examples for contrastive learning at all levels. We develop a label-aware joint model to leverage label semantics for cross-lingual knowledge transfer.
  arXiv  Detail & Related papers  (2022-05-07T13:44:28Z)
• Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features [41.951988293049205]
  We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences. The proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourseWare.
  arXiv  Detail & Related papers  (2020-11-03T20:06:48Z)
• Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech [24.187382590960254]
  Children do not build their lexicon by segmenting spoken input into phonemes and then building up words from them. This suggests that the ideal way of learning a language is by starting from full semantic units. We present a simple way to introduce such information into an RNN-based model and investigate which type of boundary is the most efficient.
  arXiv  Detail & Related papers  (2020-06-15T13:20:13Z)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.