agentic-ger: terminology recovery in long-form speech using global context
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Computer Science > Computation and Language
arXiv:2609.29428 (cs)
[Submitted on 24 Sep 2026]
Title:agentic-ger: terminology recovery in long-form speech using global context
View a PDF of the paper titled agentic-ger: terminology recovery in long-form speech using global context, by Yanqiao Zhu and 4 other authors
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Abstract:Recent advances in speech language models have improved automatic speech recognition (ASR) for long-form audio. However, accurately and consistently transcribing domain-specific terminology remains challenging. Motivated by the world knowledge and contextual capability of large language models (LLMs), we propose Agentic-GER, an LLM-based agent for terminology correction in long-form speech. The agent uses global context from the full transcript to identify suspicious terms and resolve ambiguous hypotheses. It selectively re-transcribes the source speech to check candidate corrections, and uses accepted edits to guide subsequent decisions. Experiments with four LLMs and two ASR systems on GigaSpeechBench show consistent terminology improvements in both Chinese and English, with and without thinking. On Chinese speech, Agentic-GER achieves up to a 36.8% relative reduction in biased character error rate (B-CER) over the Whisper baseline.
| Comments: | submitted to ICASSP 2027 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.29428 [cs.CL] |
| (or arXiv:2609.29428v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29428
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled agentic-ger: terminology recovery in long-form speech using global context, by Yanqiao Zhu and 4 other authors
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