SOURCE-LINKED INTELLIGENCE
Leveraging Fine-grained Error Correction in Korean Speech Recognition for Consultation Services
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 exi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-09T08:45:20.000Z
First collected: 2026-09-20T19:52:05.078Z. This is not the publication date.