Game-Oriented ASR Error Correction via RAG-Enhanced LLM
This addresses ASR accuracy issues for gamers, but it is incremental as it adapts existing methods to a specific domain.
The paper tackles the problem of ASR errors in multiplayer online games by proposing the GO-AEC framework, which reduces character error rate by 6.22% and sentence error rate by 29.71%.
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.