HiKE：韩英代码转换语音识别的层次化评估框架

摘要

尽管多语言自动语音识别（ASR）技术已取得显著进展，但语码转换（CS）——即日常对话中常见的语言混合现象——仍是一个被严重忽视的挑战。本文介绍了HiKE：层次化韩英语码转换基准，这是首个全球可访问的韩英语码转换评估框架，旨在为多语言ASR模型提供精确的评估手段，并推动该领域的研究。所提出的框架不仅包含跨主题的高质量、自然语码转换数据，还提供了细致的借词标签及层次化的语码转换级别标注方案（词、短语、句子），共同支持对模型处理各层次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，本文表明，尽管大多数多语言ASR模型在初始阶段难以应对CS-ASR任务，但通过使用CS数据进行微调，这一能力可以得到显著提升。HiKE框架将在https://github.com/ThetaOne-AI/HiKE 上公开提供。

English

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 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 while most multilingual ASR models initially struggle with CS-ASR, this capability can be enabled through fine-tuning with CS data. HiKE will be available at https://github.com/ThetaOne-AI/HiKE.

HiKE：韩英代码转换语音识别的层次化评估框架

HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition

摘要

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