# Sylber: Syllabic Embedding Representation of Speech from Raw Audio

arXiv: https://arxiv.org/abs/2410.07168 · HTML: https://mlsanity.com/paper/2410.07168/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 69 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 30 of 48)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale](https://mlsanity.com/paper/2306.15687/) (2023)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [Direct speech-to-speech translation with discrete units](https://mlsanity.com/paper/2107.05604/) (2021)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [Text-Free Prosody-Aware Generative Spoken Language Modeling](https://mlsanity.com/paper/2109.03264/) (2021)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [WavLLM: Towards Robust and Adaptive Speech Large Language Model](https://mlsanity.com/paper/2404.00656/) (2024)
- [The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling](https://mlsanity.com/paper/2011.11588/) (2020)
- [Textually Pretrained Speech Language Models](https://mlsanity.com/paper/2305.13009/) (2023)

## Built on by (most cited first, 19 of 19)

- [Recent Advances in Discrete Speech Tokens: A Review](https://mlsanity.com/paper/2502.06490/) (2025)
- [DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models](https://mlsanity.com/paper/2410.24177/) (2024)
- [Why Do Speech Language Models Fail to Generate Semantically Coherent Outputs? A Modality Evolving Perspective](https://mlsanity.com/paper/2412.17048/) (2024)
- [Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces](https://mlsanity.com/paper/2603.12642/) (2026)
- [ZeroSyl: Simple Zero-Resource Syllable Tokenization for Spoken Language Modeling](https://mlsanity.com/paper/2602.15537/) (2026)
- [Unlocking Temporal Flexibility: Neural Speech Codec with Variable Frame Rate](https://mlsanity.com/paper/2505.16845/) (2025)
- [Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models](https://mlsanity.com/paper/2505.17446/) (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)
- [Sylber 2.0: A Universal Syllable Embedding](https://mlsanity.com/paper/2601.22306/) (2026)
- [MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances](https://mlsanity.com/paper/2509.17143/) (2025)
- [Revisiting Lexicon Evaluation in Unsupervised Word Discovery](https://mlsanity.com/paper/2606.06183/) (2026)
- [findsylls: A Language-Agnostic Toolkit for Syllable-Level Speech Tokenization and Embedding](https://mlsanity.com/paper/2603.26292/) (2026)
- [SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment](https://mlsanity.com/paper/2604.16445/) (2026)
- [Recovering the Zipfian Distribution in Unsupervised Term Discovery](https://mlsanity.com/paper/2606.10781/) (2026)
- [On the Effect of Segmentation Width and Cluster Size on Speech Resynthesis and Continuation in Generative Spoken Language Models](https://mlsanity.com/paper/2606.23285/) (2026)
- [Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization](https://mlsanity.com/paper/2607.04064/) (2026)
- [StreamAlign: Streaming Text-Aligned Speech Tokenization](https://mlsanity.com/paper/2609.09719/) (2026)
- [Distributional Metrics for Evaluating Spoken Conversational Systems](https://mlsanity.com/paper/2609.31719/) (2026)

## Abstract (arXiv)

Syllables are compositional units of spoken language that efficiently structure human speech perception and production. However, current neural speech representations lack such structure, resulting in dense token sequences that are costly to process. To bridge this gap, we propose a new model, Sylber, that produces speech representations with clean and robust syllabic structure. Specifically, we propose a self-supervised learning (SSL) framework that bootstraps syllabic embeddings by distilling from its own initial unsupervised syllabic segmentation. This results in a highly structured representation of speech features, offering three key benefits: 1) a fast, linear-time syllable segmentation algorithm, 2) efficient syllabic tokenization with an average of 4.27 tokens per second, and 3) novel phonological units suited for efficient spoken language modeling. Our proposed segmentation method is highly robust and generalizes to out-of-domain data and unseen languages without any tuning. By training token-to-speech generative models, fully intelligible speech can be reconstructed from Sylber tokens with a significantly lower bitrate than baseline SSL tokens. This suggests that our model effectively compresses speech into a compact sequence of tokens with minimal information loss. Lastly, we demonstrate that categorical perception-a linguistic phenomenon in speech perception-emerges naturally in Sylber, making the embedding space more categorical and sparse than previous speech features and thus supporting the high efficiency of our tokenization. Together, we present a novel SSL approach for representing speech as syllables, with significant potential for efficient speech tokenization and spoken language modeling.