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arXiv · 2509.23630

Game-Oriented ASR Error Correction via RAG-Enhanced LLM

Yan Jiang·Yongle Luo·Qixian Zhou·Elvis S. Liu
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Abstract

With the rise of multiplayer online games, real-time voice communication is essential for team coordination. However, general ASR systems struggle with gaming-specific challenges like short phrases, rapid speech, jargon, and noise, leading to frequent errors. To address this, we propose the GO-AEC framework, which integrates large language models, Retrieval-Augmented Generation (RAG), and a data augmentation strategy using LLMs and TTS. GO-AEC includes data augmentation, N-best hypothesis-based correction, and a dynamic game knowledge base. Experiments show GO-AEC reduces character error rate by 6.22% and sentence error rate by 29.71%, significantly improving ASR accuracy in gaming scenarios.

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Yan Jiang, Yongle Luo, Qixian Zhou, Elvis S. Liu. 2025-09-28. Game-Oriented ASR Error Correction via RAG-Enhanced LLM. https://doi.org/10.1109/cog64752.2025.11114204

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