PairAlign: New Framework for Audio Tokenization Using Self-Aligned Sequence Generation
Researchers have introduced PairAlign, a framework that tokenizes audio through sequence-level self-alignment rather than traditional quantization or codec-based methods. Unlike existing audio tokenizers that assign tokens locally, PairAlign treats tokenization as conditional sequence generation, training an autoregressive decoder to learn token identity, order, length, and sequence boundaries. The approach reduces archive token count by 55% on retrieval tasks while preserving edit-distance search quality, potentially improving efficiency in audio-based machine learning systems.
PairAlign is a proposed audio tokenization framework that addresses limitations in current methods, which rely on quantization, clustering, or codec reconstruction and rarely optimize for sequence consistency, compactness, length control, or edit similarity. The system uses an encoder to map speech to a continuous representation and an autoregressive decoder to generate discrete token sequences, learning from two content-preserving views of the same audio such that each view's token sequence is trained to be probable under the other's representation. Competing sequences from unrelated examples discourage many-to-one collapse, providing a scalable surrogate for edit-distance preservation. The framework builds on vector-quantization-style tokenization and refines it with several techniques including EMA-teacher targets, cross-paired teacher forcing, prefix corruption, likelihood contrast, and length control. On 3-second speech samples, PairAlign produces compact, non-degenerate sequences with broad vocabulary usage and strong cross-view consistency, and reduces archive token count by 55% on retrieval benchmarks. The authors describe PairAlign as a sequence-symbolic predictive learner analogous to JEPA-style objectives, but operating over learned variable-length symbolic sequences rather than continuous latents. The paper is a preprint currently under review, comprising 57 pages of main content.
What's missing
As a preprint under review, the work has not yet undergone peer review. The paper does not report evaluations on downstream tasks beyond retrieval (e.g., speech recognition or generation quality), leaving open questions about how PairAlign's tokenization affects performance in broader applications. Scalability to longer audio sequences beyond 3-second clips is not addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization
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