Despite advances in multilingual automatic speech recognition (ASR), code-switching (CS), the mixing of languages within an utterance common in daily speech, remains a severely underexplored challenge. In this paper, we introduce HiKE: the Hierarchical Korean-English code-switching benchmark, the first globally accessible non-synthetic evaluation framework for Korean-English CS, aiming to provide a means for the precise evaluation of multilingual ASR models and to foster research in the field. The proposed framework not only consists of high-quality, natural CS data across various topics, but also provides meticulous loanword labels and a hierarchical CS-level labeling scheme (word, phrase, and sentence) that together enable a systematic evaluation of a model's ability to handle each distinct level of code-switching. Through evaluations of diverse multilingual ASR models and fine-tuning experiments, this paper demonstrates that although most multilingual ASR models initially exhibit inadequate CS-ASR performance, this capability can be enabled through fine-tuning with synthetic CS data. HiKE is available at https://github.com/ThetaOne-AI/HiKE.

翻译：尽管多语言自动语音识别（ASR）技术取得了进展，但语码转换（CS）——即日常口语中常见的同一话语内混合使用多种语言的现象——仍然是一个研究严重不足的挑战。本文提出HiKE：分层韩英语码转换基准，这是首个全球可访问的非合成韩英语码转换评估框架，旨在为多语言ASR模型提供精确评估手段，并推动该领域的研究。所提出的框架不仅包含跨多个主题的高质量自然语码转换数据，还提供了细致的借词标签以及分层的语码转换级别标注方案（词级、短语级和句子级），这些共同支持对模型处理不同层级语码转换能力的系统性评估。通过对多种多语言ASR模型的评估及微调实验，本文证明尽管大多数多语言ASR模型初始的语码转换语音识别性能不足，但通过使用合成语码转换数据进行微调可有效激活该能力。HiKE已在https://github.com/ThetaOne-AI/HiKE开源发布。