Game-Oriented ASR Error Correction via RAG-Enhanced LLM

With the rapid development of the gaming industry and the increasing popularity of multiplayer online games, realtime voice communication has become a crucial tool for team collaboration and tactical exchanges. Automatic Speech Recognition (ASR) technology plays a vital role in modern gaming by converting voice commands into text, enabling efficient communication among players. However, existing general-purpose ASR systems face significant challenges in gaming scenarios due to the unique characteristics of in-game communication, such as short phrases, rapid speech, game-specific jargon, and environmental noise. These limitations often lead to frequent recognition errors, increasing communication costs and negatively impacting the overall gaming experience. Furthermore, the scarcity of domainspecific ASR training data exacerbates these issues, hindering system optimization. To address the challenges of ASR systems in gaming scenarios, this study proposes the GO-AEC (Gaming-Oriented ASR Error Correction) framework. The framework leverages the generative capabilities of large language models (LLMs) and employs Retrieval-Augmented Generation (RAG) techniques with a gamespecific knowledge base to better adapt to gaming environments. Additionally, we introduce a data augmentation strategy that combines LLMs with text-to-speech (TTS) techniques to enhance the diversity and robustness of game-specific datasets. The GO-AEC framework consists of three key modules: the data augmentation module, the N-best hypothesis-based LLM correction module, and the dynamic knowledge base module powered by RAG. Experimental results demonstrate that, compared to baseline methods, the proposed framework reduces the character error rate (CER) by 6.22 % and the sentence error rate (SER) by 29.71 %. These findings indicate that the GO-AEC framework effectively addresses the challenges of ASR error correction in gaming scenarios.