REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners or inference-time post-processing. We show that these generated timestamps can drift across long non-speech spans: the transcript may remain plausible, but the decoded time axis drifts away from the audio. We study this non-speech-induced timestamp drift with self-built gap and long-gap benchmarks across 15 evaluated timestamp-producing ASR and audio-language systems. Naive timestamp-corrected fine-tuning improves alignment but can severely degrade non-target
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- PossiblePossibly related (embedding) · 49%lgy1027/matrix-live-diarizer →
- PossiblePossibly related (embedding) · 45%Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World →
- LinkedLinked via arxiv author · 85%Cheng-Kang Chou →
“REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing”
- LinkedLinked via arxiv author · 85%Ming-To Chuang →
“REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing”
- LinkedLinked via arxiv author · 85%Ke-Han Lu →
“REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing”
- LinkedLinked via arxiv author · 85%Chan-Jan Hsu →
“REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing”
- LinkedLinked via arxiv author · 85%Hung-yi Lee →
“REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing”
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
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