Efficient Punctuation Restoration via Weighted Lookahead Scoring Method for Streaming ASR Systems
Punctuation restoration improves ASR (Automatic Speech Recognition) readability. However streaming ASR requires online decisions with limited future context. In streaming ASR, the system predicts punctuation incrementally, which makes generation-based approaches prone to latency and alignment failures under boundary-wise evaluation. This paper proposes a non-autoregressive scoring method (no free-form generation) that preserves the input transcript and makes a decision at each word boundary. Our method compares punctuation insertion hypotheses against a no-insertion baseline under a bounded K-subword-token lookahead, and calibrates decisions using a weight α and a validation-calibrated th...
| Authors | Sungmook Woo, Hyungu Kang, Chanwoo Kim |
|---|---|
| Primary category | cs.CL |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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