Acoustic Progress Propagation for Long-Horizon Speculative Decoding in ASR
Speculative decoding accelerates autoregressive automatic speech recognition (ASR), but the acceptance length of alignment-aware drafters can saturate as the draft horizon increases. We propose a progress-aware speculative drafter that recurrently propagates an acoustic progress state across draft steps and feeds it back into audio cross-attention to guide token generation. We jointly train the drafter and progress predictor over variable draft horizons. On five ASR test sets, our method achieves lossless, macro-averaged end-to-end speedups of 1.657x and 1.227x over target-only autoregressive decoding with Qwen3-ASR-0.6B and Qwen3-ASR-1.7B, respectively. Relative to AnchorDraft, our metho...
| Authors | Yuanyuan Jia, Qianqian Yang |
|---|---|
| Primary category | eess.AS |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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