Game-Oriented ASR Error Correction via RAG-Enhanced LLM
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908563062915072 |
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| author | Jiang, Yan Luo, Yongle Zhou, Qixian Liu, Elvis S. |
| author_facet | Jiang, Yan Luo, Yongle Zhou, Qixian Liu, Elvis S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Game-Oriented ASR Error Correction via RAG-Enhanced LLM Jiang, Yan Luo, Yongle Zhou, Qixian Liu, Elvis S. Artificial Intelligence 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. |
| title | Game-Oriented ASR Error Correction via RAG-Enhanced LLM |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23630 |