REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
Score
80揭示了ASR系统时间戳漂移的新问题并提出有效解决框架，对领域有增量贡献。
实验设计严谨，基准覆盖广泛，结果数据详实，方法描述清晰。
仅需少量数据与参数更新即可显著提升时间对齐精度，适合实际部署。
首发于arXiv，时间戳漂移为近期ASR应用热点，具有很强时效性。
来自arXiv预印本，作者单位可信，且方法经过充分实验验证。
Base 88.5 × T2 Source weight ×0.9 = 80
Curation line 70Selected
Why selected
Innovatively proposes a lightweight post-training framework combining replay and distribution editing, with thorough experiments and significant improvements, high practical value.
AI commentary
「This paper is highly recommended for ASR practitioners, especially those working on long-form audio or real-time captioning. It tackles a common yet often overlooked problem of timestamp drift in deployment, with a lightweight method that requires no expensive annotations. If you are using Whisper or other end-to-end ASR models, REDDIT's approach can be directly adapted to improve temporal alignment accuracy.」
Quick overview
Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners. However, these timestamps drift across long non-speech spans. This work proposes REDDIT (Replay-based Distribution Editing), a lightweight two-stage post-training framework that corrects timestamp drift while avoiding catastrophic forgetting. Using only 34.9 hours of targeted correction audio and updating 1.6% of model parameters, it improves long-gap mIoU from 38.7% to 95.0%, reduces mixed-gap out-of-domain AAS from 2752 ms to 223 ms, and preserves CV-en MER at 41.3%.
- Autoregressive ASR systems exhibit timestamp drift across long non-speech spans, with plausible transcripts but misaligned time axes.
- Naive timestamp-corrected fine-tuning causes catastrophic forgetting, degrading non-target ASR behavior.
- REDDIT edits timestamp targets under replayed decoder context while matching frozen base distribution, requiring no human transcripts or timestamp annotations.
- On Whisper-tiny, only 1.6% of parameters updated; long-gap mIoU raised to 95.0% and drift nearly eliminated.
Full text
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