HiKE：韓英語碼轉換語音識別的層次化評估框架

摘要

儘管多語言自動語音識別（ASR）技術已取得進展，但語碼轉換（CS）——即日常對話中常見的語言混合現象——仍是一個嚴重未被充分探索的挑戰。本文介紹了HiKE：分層韓英語碼轉換基準，這是首個全球可訪問的韓英CS評估框架，旨在為多語言ASR模型提供精確的評估手段，並推動該領域的研究。所提出的框架不僅包含跨多種主題的高質量、自然CS數據，還提供了細緻的外來詞標籤及分層CS級別標籤方案（詞、短語和句子），這些共同促成了對模型處理各級別語碼轉換能力的系統性評估。通過對多種多語言ASR模型的評估及微調實驗，本文表明，雖然大多數多語言ASR模型最初在CS-ASR上表現不佳，但通過使用CS數據進行微調，可顯著提升此能力。HiKE將在https://github.com/ThetaOne-AI/HiKE上公開提供。

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：韓英語碼轉換語音識別的層次化評估框架

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

摘要

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