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

Fadel Thior, Ahmer Tabassum, Haania Siddiqui, Hanan Aldarmaki, Thamar Solorio · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31415 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

代码交换(CS)自动语音识别(ASR)仍然是困难的,因为真正的CS语音很少,合成语音并不完全匹配它。我们调查合成CS语音是否对学习适应友好的初始化更有用,而不是普通的连续精简。我们提出SyRA-CS,基于爬行动物元学的对称合成语音对真实的适应框架。对于每个语言对和预先训练的模型,我们对相应的合成CS-FLEURS子集更有用,然后使用一个小时标记的真实目标语音进行调整。我们评估了四个架构设置: Encoder-CTC,FastConformer-NN-T,Attention Encoder-Decoder (AED)和Speech-MLL。实验涵盖了阿拉伯语,法语

Machine translation (Meta M2M100); the English original follows.

Abstract

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. Experiments cover on Arabic-, French-, German-, and Hindi-English, against synthetic-only transfer, real-only fine-tuning, and continual fine-tuning. SyRA-CS improves over continual fine-tuning on WER in all 16 model-pair comparisons and on CER in 15 of 16, with relative macro WER reductions of 17.7% for XLS-R and 5.3-5.8% for the other three models. The gains hold across the tested models but vary in size across architectures, language pairs and metrics.

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Topics: Computer science, Speech recognition, Initialization, Adaptation (eye), Language model, Artificial intelligence

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