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arXiv · 2501.09512

PIER: A Novel Metric for Evaluating What Matters in Code-Switching

Enes Yavuz Ugan·Ngoc-Quan Pham·Leonard Bärmann·Alex Waibel
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Abstract

Code-switching, the alternation of languages within a single discourse, presents a significant challenge for Automatic Speech Recognition. Despite the unique nature of the task, performance is commonly measured with established metrics such as Word-Error-Rate (WER). However, in this paper, we question whether these general metrics accurately assess performance on code-switching. Specifically, using both Connectionist-Temporal-Classification and Encoder-Decoder models, we show fine-tuning on non-code-switched data from both matrix and embedded language improves classical metrics on code-switching test sets, although actual code-switched words worsen (as expected). Therefore, we propose Point-of-Interest Error Rate (PIER), a variant of WER that focuses only on specific words of interest. We instantiate PIER on code-switched utterances and show that this more accurately describes the code-switching performance, showing huge room for improvement in future work. This focused evaluation allows for a more precise assessment of model performance, particularly in challenging aspects such as inter-word and intra-word code-switching.

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Enes Yavuz Ugan, Ngoc-Quan Pham, Leonard Bärmann, Alex Waibel. 2025-01-16. PIER: A Novel Metric for Evaluating What Matters in Code-Switching. https://arxiv.org/abs/2501.09512

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