REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
arXiv:2607.05364v1 Announce Type: cross Abstract: 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 impr
The proliferation of autoregressive ASR systems requiring accurate timestamping, especially for long-form content, necessitates solutions for drift correction to enhance real-world utility.
Accurate timestamping in ASR is crucial for media indexing, content creation, and accessibility, directly impacting the effectiveness and reliability of AI-driven audio processing applications.
This research outlines a method to significantly improve the practical reliability of timestamped ASR outputs, making such systems more robust for real-world deployment across various industries.
- · AI-powered media platforms
- · Content creators
- · Accessibility technology providers
- · ASR system developers
- · ASR systems with poor timestamping
Improved accuracy and reliability of AI-generated timestamps for audio transcription services.
Enhanced productivity in media editing and content analysis workflows that rely on precise audio alignment.
Accelerated adoption of ASR technologies in new applications benefiting from robust, timestamped output, potentially reducing manual work for various tasks.
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Read at arXiv cs.AI