PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization
arXiv:2605.06582v2 Announce Type: replace Abstract: Many operations on sensory data -- comparison, memory, retrieval, and reasoning -- are naturally expressed over discrete symbolic structures. In language this interface is given by tokens; in audio, it must be learned. Existing audio tokenizers rely on quantization, clustering, or codec reconstruction, assigning tokens locally, so sequence consistency, compactness, length control, termination, and edit similarity are rarely optimized directly. We introduce PairAlign, a framework for compact audio tokenization through sequence-level self-align
The proliferation of generative AI models across modalities necessitates more efficient and consistent data representation for non-textual data like audio, making advanced tokenization a current research priority.
Improved audio tokenization can unlock more powerful and efficient AI models for processing sensory data, broadening the scope of AI applications and improving their performance.
Current audio tokenization methods that rely on local assignments are being challenged by sequence-level self-alignment, promising more coherent, compact, and controllable audio representations.
- · AI researchers and developers
- · Companies active in audio processing AI
- · Users of AI applications with audio interfaces
- · Legacy audio tokenization methods
- · AI models that struggle with inefficient audio inputs
More accurate and efficient AI models for speech, music, and environmental sound will emerge.
New applications in areas like AI-driven content generation, accessibility tools, and surveillance could become viable.
The ability to seamlessly integrate audio into multimodal AI systems could accelerate the development of more human-like AI agents.
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Read at arXiv cs.LG