为韩国气象专家优化语音识别，提升天气预报效率。
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts
- 构建韩语气象领域语音查询数据集，解决专业术语识别难题。
- 通过语音合成增强数据，使专有词汇识别率显著提升。
- 适合气象AI、语音识别与垂直领域应用研究者参考。
本文探索将自动语音识别（ASR）集成到自然语言查询系统中，以提升韩国气象学家的天气预报效率。针对韩语气象领域的专业词汇和语言复杂性，我们采集了母语者录制的语音查询数据，构建了一个评估数据集。基于该数据集，评估了多种多语言ASR模型配置，发现其在领域术语上的识别性能受限。随后采用基于文本转语音的数据增强方法，有效提升了专业术语识别效果，同时保持通用领域表现。本工作贡献包括创建领域专用数据集、全面的ASR模型评估以及一种高效的增强技术，为韩语气象ASR的未来发展奠定基础。
原文摘要 · Abstract (English)
This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We address challenges in developing ASR systems for the Korean weather domain, specifically specialized vocabulary and Korean linguistic intricacies. To tackle these issues, we constructed an evaluation dataset of spoken queries recorded by native Korean speakers. Using this dataset, we assessed various configurations of a multilingual ASR model family, identifying performance limitations related to domain-specific terminology. We then implemented a simple text-to-speech-based data augmentation method, which improved the recognition of specialized terms while maintaining general-domain performance. Our contributions include creating a domain-specific dataset, comprehensive ASR model evaluations, and an effective augmentation technique. We believe our work provides a foundation for future advancements in ASR for the Korean weather forecasting domain.
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