G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition
We study timestamped speaker-attributed automatic speech recognition (SA-ASR) for long-form, multi-party speech with overlap. In this setting, chunk-wise inference must preserve meeting-level speaker identity consistency while producing time-stamped, speaker-labeled transcripts. Prior Speech-LLM systems tend to prioritize either local diarization or global labeling, lacking the ability to jointly model fine-grained temporal boundaries and robust cross-chunk identity linking. We propose G-STAR, an end-to-end framework that couples a cache-conditioned speaker-tracking module with a Speech-LLM transcription backbone. The tracker provides structured speaker cues with temporal grounding, and t...
| Authors | Jing Peng, Ziyi Chen, Haoyu Li et al. |
|---|---|
| Primary category | eess.AS |
| Published | 11 March 2026 |
| Last updated at source | 29 May 2026 |
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