This work studies whether code-switching automatic speech recognition (CS-ASR) can move beyond pair-specific systems and generalize to language pairs that were not seen during training.
The paper addresses a scalability challenge in multilingual ASR: as the number of supported languages grows, building separate CS-ASR support for every bilingual pair becomes increasingly impractical. It evaluates whether bilingual CS-ASR capabilities can transfer across language pairs through model merging and domain generalization methods.
Key highlights:
- Frames unseen language-pair generalization as a central challenge for truly multilingual CS-ASR.
- Studies whether capabilities learned from limited seen language pairs transfer to unseen language pairs.
- Evaluates model merging and domain generalization approaches for improving scalability.
- Finds that merged bilingual CS-ASR models modestly generalize to unseen pairs, indicating limited but meaningful transfer.