EntangleCodec: A Unified Discrete Audio Tokenizer via Semantic-Acoustic Entanglement
Audio tokenizers serve as the discrete interface between continuous audio and Audio Language Models (ALMs), but existing tokenizers often struggle to support both understanding and generation. Reconstruction-oriented codecs preserve acoustic fidelity but lack rich semantics, while semantic-aware tokenizers typically rely on separate semantic and acoustic streams, introducing redundancy or misalignment. We propose \textbf{EntangleCodec}, a unified discrete audio tokenizer that learns caption-aligned semantic-acoustic representations before quantization. By aligning audio with rich captions rather than ASR transcripts, EntangleCodec captures linguistic content, speaker identity, emotion, pr...
| Authors | Hui Li, Yangfan Gao, Junlin Shang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 1 June 2026 |
| Last updated at source | 3 September 2026 |
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