@inproceedings{peng-etal-2026-g,
title = "{G}-{STAR}: End-to-End Global Speaker-Tracking Attributed Recognition",
author = "Peng, Jing and
Chen, Ziyi and
Li, Haoyu and
Wang, Yucheng and
Li, Mengtian and
MA, Duo and
Xi, Yu and
Du, Yunfan and
Yu, Kai and
Wang, Shuai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1697/",
pages = "36267--36276",
ISBN = "979-8-89176-462-0",
abstract = "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 the LLM generates attributed text conditioned on these cues. G-STAR supports component-wise optimization and joint end-to-end training, enabling flexible learning under heterogeneous supervision and domain shift. Under chunk-wise decoding protocols, experiments on both oracle-segmented local evaluation and full-meeting global evaluation show strong speaker-attributed transcription performance."
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<abstract>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 the LLM generates attributed text conditioned on these cues. G-STAR supports component-wise optimization and joint end-to-end training, enabling flexible learning under heterogeneous supervision and domain shift. Under chunk-wise decoding protocols, experiments on both oracle-segmented local evaluation and full-meeting global evaluation show strong speaker-attributed transcription performance.</abstract>
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%0 Conference Proceedings
%T G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition
%A Peng, Jing
%A Chen, Ziyi
%A Li, Haoyu
%A Wang, Yucheng
%A Li, Mengtian
%A MA, Duo
%A Xi, Yu
%A Du, Yunfan
%A Yu, Kai
%A Wang, Shuai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F peng-etal-2026-g
%X 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 the LLM generates attributed text conditioned on these cues. G-STAR supports component-wise optimization and joint end-to-end training, enabling flexible learning under heterogeneous supervision and domain shift. Under chunk-wise decoding protocols, experiments on both oracle-segmented local evaluation and full-meeting global evaluation show strong speaker-attributed transcription performance.
%U https://aclanthology.org/2026.findings-emnlp.1697/
%P 36267-36276
Markdown (Informal)
[G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition](https://aclanthology.org/2026.findings-emnlp.1697/) (Peng et al., Findings 2026)
ACL
- Jing Peng, Ziyi Chen, Haoyu Li, Yucheng Wang, Mengtian Li, Duo MA, Yu Xi, Yunfan Du, Kai Yu, and Shuai Wang. 2026. G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 36267–36276, Budapest, Hungary. Association for Computational Linguistics.