REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
The episode discusses the REDDIT paper, which addresses timestamp drift in Automatic Speech Recognition models. Using a two-stage replay-based approach, researchers correct timing errors without degrading speech recognition accuracy. This method utilizes synthetic data and minimal parameter updates to significantly improve temporal precision while avoiding model "forgetting."