SyRA-CS: Synthetic-to-Real Adaptation for Code-Switched ASR with Meta-Learning
Code-switched (CS) automatic speech recognition (ASR) remains difficult because real CS speech is scarce and synthetic speech does not fully match it. We investigate whether synthetic CS speech is more useful for learning an adaptation-friendly initialization than for ordinary continual fine-tuning. We propose SyRA-CS, a pair-specific synthetic-to-real adaptation framework based on Reptile meta-learning. For each language pair and pretrained model, we meta-train on the corresponding synthetic CS-FLEURS subset and then adapt using one hour of labeled real target speech. We evaluate four architectural settings: Encoder-CTC, FastConformer-RNN-T, Attention Encoder-Decoder (AED) and Speech-LLM...
| Authors | Fadel Thior, Ahmer Tabassum, Haania Siddiqui et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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