PairAlign learns compact variable-length token sequences for audio via self-alignment on paired content-preserving views, achieving 55% fewer archive tokens than VQ while preserving edit-distance retrieval at 12.71 tokens/s.
Best-std: Bidirectional mamba-enhanced speech tokenization for spoken term detection
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
wav2tok 2.0 improves audio tokenization for query-by-example spoken term detection via staged training that first learns speaker-invariant representations then enforces pairwise token alignment.
citing papers explorer
-
PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization
PairAlign learns compact variable-length token sequences for audio via self-alignment on paired content-preserving views, achieving 55% fewer archive tokens than VQ while preserving edit-distance retrieval at 12.71 tokens/s.
-
wav2tok 2.0: Scalable Audio Tokenization Maintaining Explicit Pairwise Token Alignment for Efficient Audio Retrieval
wav2tok 2.0 improves audio tokenization for query-by-example spoken term detection via staged training that first learns speaker-invariant representations then enforces pairwise token alignment.