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About this paper
Cheol Jun Cho, Nicholas Lee, Alan W Black, Gopala K. Anumanchipalli
Sylber 2.0 provides a framework for learning universal syllable embeddings directly from raw audio across 102 languages and diverse styles. It achieves near-lossless speech reconstruction at an average token frequency of 4.8 Hz, significantly improving efficiency while preserving linguistic content and acoustic details.
Key insights
- Self-supervised learning, incorporating multi-stage distillation, can effectively induce emergent and universal syllabic structures from raw speech across diverse languages.
- Disentangling linguistic content and acoustic information into separate syllable embeddings (C and A tokens) is crucial for achieving high-fidelity reconstruction and flexible control in generative speech models.
- Syllables serve as a naturally efficient and linguistically grounded unit for speech tokenization, enabling significant temporal compression without compromising intelligibility or acoustic richness.
Summary by alphaXiv.
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