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This work provides a systematic analysis protocol for studying length-based training in variable-length speech models through matched comparisons, and results are consistent with a link between chunk-length variation and token weighting.
This work proposes Reused Encoder States for Verifying Events (REVE), a lightweight method that uses states already computed by the target model that achieves a reduction comparable to those of CED-Tiny and CED-Base.
Likelihood-Constrained Acoustic Reranking (LCAR), a training-free decoding method that improves acoustic grounding while preserving support from the base model, is proposed and evaluated on four LLM-based ASR systems using human-audited TTS and open-source speech challenge suites.
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