The content introduces a new benchmark to evaluate the code-switching capabilities of self-supervised speech encoders. It showcases experiments with various speech encoders on different code-switched language pairs. The study highlights the impact of pre-training languages and model size on benchmark performance. Results indicate that multilingual pre-training enhances code-switching abilities, but there is still room for improvement. The proposed task requires models to distinguish correct from incorrect speech utterances, emphasizing syntactic and semantic understanding in a code-switching scenario. Data generation, validation, statistics, baseline systems, and evaluation metrics are discussed comprehensively.
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