HiKE: Hierarchical Evaluation Framework for Korean-English
  Code-Switching Speech Recognition
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.
68de41e4a9129e507bf4a4a1
Gio Paik
claimed_verified
2025-10-06T12:49:08.517Z
66976a7fa7fd582ae754850e
https://cdn-avatars.huggingface.co/v1/production/uploads/66976a7fa7fd582ae754850e/jQXfIqKh1FYGxjyFwSmSY.jpeg
Gio Paik
user
skyil7
68de41e4a9129e507bf4a4a2
Yongbeom Kim
68de41e4a9129e507bf4a4a3
Soungmin Lee
claimed_verified
2025-10-07T12:29:01.544Z
67470f4acccc7c0cdb9a4859
https://cdn-avatars.huggingface.co/v1/production/uploads/67470f4acccc7c0cdb9a4859/6V1ae4bY3xyA-gFXaa2G4.jpeg
Soungmin Lee
user
soungminlee
68de41e4a9129e507bf4a4a4
Sangmin Ahn
68de41e4a9129e507bf4a4a5
Chanwoo Kim
HiKE: Hierarchical Evaluation Framework for Korean-English
  Code-Switching Speech Recognition
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.
A hierarchical benchmark for Korean-English code-switching in ASR evaluates model performance and demonstrates improvement through fine-tuning with code-switched data.
multilingual automatic speech recognition code-switching HiKE Hierarchical Korean-English code-switching benchmark loanword labels hierarchical CS-level labeling scheme fine-tuning