Replay-based method fixes timestamp drift in autoregressive speech models
Autoregressive ASR systems that emit timestamps as decoded tokens face a critical alignment problem: during long silences, the time axis drifts even when transcription remains accurate. Researchers introduce REDDIT, a replay-based post-training method that corrects this drift without catastrophic forgetting of core ASR performance. The work exposes a fundamental tension in fine-tuning speech models: naive correction breaks downstream behavior. This matters because timestamped transcription is becoming standard in production systems, and the forgetting problem signals broader challenges in targeted model editing across multimodal tasks.
Modelwire context
ExplainerThe core insight isn't just that REDDIT fixes timestamp drift, but that it exposes a fundamental asymmetry: correcting one learned behavior (time alignment) during post-training actively degrades another (transcription accuracy). This signals that targeted edits to multimodal models may require architectural changes, not just better fine-tuning recipes.
This connects directly to the forgetting audit framework from early July, which revealed that naive deletion-based unlearning masks persistent knowledge pathways through parametric leakage and alternative retrieval routes. REDDIT faces an analogous problem in the opposite direction: when you suppress timestamp drift, the model's core transcription capability leaks away. Both papers expose that surface-level metrics (timestamp alignment, fact deletion) obscure what's actually happening in the weights. The difference is scope: the audit paper studied knowledge deletion in memory-externalized systems, while REDDIT tackles alignment correction in autoregressive speech models, but the underlying tension is identical.
If production ASR deployments adopt REDDIT and report that timestamp accuracy improves without regression on word error rate across held-out speakers and acoustic conditions, the method is robust. If accuracy holds only on the training distribution or requires per-domain tuning, it signals the forgetting problem is domain-specific rather than fundamental, which would narrow the implications for other multimodal editing tasks.
Coverage we drew on
- Auditing Forgetting in Limited Memory Language Models · arXiv cs.CL
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MentionsREDDIT · ASR systems · autoregressive models
Modelwire Editorial
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.