HiKE: Hierarchical Evaluation Framework for Korean-English
  Code-Switching Speech Recognition
Authors: name: Gio Paik, name: Yongbeom Kim, name: Soungmin Lee, name: Sangmin Ahn, name: Chanwoo Kim
Organization: Theta One AI

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.