Replay-Based Editing Reduces Timestamp Drift in Autoregressive ASR

A new study examines how autoregressive speech recognition systems that output timestamps as decoded tokens can gradually lose alignment during long stretches without speech. The authors propose a replay-based distribution editing approach that corrects this drift while limiting forgetting of previously learned behavior. The work targets timestamped transcription without relying on frame-level aligners or inference-time fixes.