Leveraging Fine-grained Error Correction in Korean Speech Recognition for Consultation Services
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Computer Science > Computation and Language
arXiv:2609.09889 (cs)
[Submitted on 9 Sep 2026]
Title:Leveraging Fine-grained Error Correction in Korean Speech Recognition for Consultation Services
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Abstract:Automatic Speech Recognition (ASR) technology is fundamental to customer service automation and large-scale transcription. However, even advanced ASR models exhibit inevitable errors in complex real-world environments such as call center conversations. When privacy restrictions preclude audio access, error correction must rely on text-based post-editing. Existing text-only approaches face significant challenges in low-resource languages, mainly due to a critical scarcity of annotated corpora and tailored correction methodologies. For Korean, this resource gap is particularly pronounced, as existing resources are predominantly designed for ASR training rather than text-based error correction. To address this, we introduce DasanCallDial, the first large-scale Korean benchmark dataset specifically curated for dialogue-level ASR error correction. Derived from genuine call center interactions, it comprises 1,974 dialogues with 115,460 utterances. Leveraging this resource, we propose Detector-Gated Contextual Span Correction (DCSC), a text-only post-editing framework for error-sparse Korean speech recognition transcripts. DCSC combines an encoder-based detector that first performs token-level error detection, followed by a language model-based corrector trained to rectify fine-grained span-level errors. Additionally, we employ dialogue-level context augmentation to enable the model to leverage discourse history for disambiguation. By employing multi-level granularity, our method achieves state-of-the-art performance, effectively overcoming the limitations of general LLMs in low-resource settings.
| Comments: | Published in Engineering Applications of Artificial Intelligence |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.09889 [cs.CL] |
| (or arXiv:2609.09889v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.09889
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Engineering Applications of Artificial Intelligence 183 (2026) 116038 |
| Related DOI: | https://doi.org/10.1016/j.engappai.2026.116038
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View a PDF of the paper titled Leveraging Fine-grained Error Correction in Korean Speech Recognition for Consultation Services, by Yonghyun Jun and 5 other authors
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