arXiv:2606.02739v1 Announce Type: cross Abstract: 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 quant
The rapid advancement of Audio Language Models (ALMs) creates an immediate need for more sophisticated and unified audio tokenization methods that can bridge the gap between acoustic fidelity and semantic understanding.
This development addresses a critical bottleneck in AI audio processing, potentially unlocking more powerful and versatile ALMs capable of both nuanced understanding and high-fidelity generation.
The previous trade-off between semantic richness and acoustic precision in audio tokenizers is reduced, leading to AI systems that can better interpret and create audio content.
- · AI researchers
- · Audio Language Model developers
- · Voice AI companies
- · Generative AI platforms
- · Developers relying on dual-stream audio processing architectures
- · Companies with less unified audio tokenizer approaches
Improved performance and efficiency for audio-based AI applications, from speech recognition to music generation.
Acceleration of research into more natural and human-like AI audio interfaces and content creation tools.
Enhanced AI capabilities in areas requiring deep audio understanding, such as context-aware virtual assistants or advanced audio forensics.
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Read at arXiv cs.AI