Look Less, Hear Better: Jointly Rewarded GRPO for Streaming ASR
Streaming automatic speech recognition (ASR) must be judged jointly on what it transcribes and on how quickly it commits each word. Delayed streams modeling (DSM) has become the dominant paradigm for streaming large audio-language models, exposing a structural delay $τ$ that bounds the decoder's lookahead. We show that $τ$ is a poor proxy for user-perceived latency, and that the alignment-based supervision of DSM leaves latency on the table: the same forced-aligned transcript is used at every $τ$, forcing the model to withhold words it could already commit. We introduce AWED, a word-level emission-delay metric defined relative to the acoustic end of each word, and post-train a DSM recogni...
| Authors | Xiuwen Zheng |
|---|---|
| Primary category | cs.SD |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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