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arXiv Computation and Language By Fadel Thior, Ahmer Tabassum, Haania Siddiqui, Hanan Aldarmaki, Thamar Solorio

SyRA-CS: Synthetic-to-Real Adaptation for Code-Switched ASR with Meta-Learning

Read the original on arXiv Computation and Language →

SyRA-CS is a synthetic-to-real adaptation framework for code‑switched ASR that uses Reptile meta‑learning to create pair‑specific initializations from synthetic CS speech. For each language pair and pretrained model, the system meta‑trains on a synthetic CS‑FLEURS subset and then adapts with just one hour of labeled real target speech. Across four architectural settings and four language pairs, SyRA-CS consistently outperforms continual fine‑tuning, achieving up to 17.7% relative WER reduction for XLS‑R and 5.3–5.8% for other models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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