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While many speakers of low-resource languages regularly code-switch betweentheir languages and other regional languages or English, datasets ofcodeswitched speech are too small to train bespoke acoustic models from scratchor do language model rescoring. Here we propose finetuning self-supervisedspeech representations such as wav2vec 2.0 XLSR to recognize code-switcheddata. We find that finetuning self-supervised multilingual representations andaugmenting them with n-gram language models trained from transcripts reducesabsolute word error rates by up to 20% compared to baselines of hybrid modelstrained from scratch on code-switched data. Our findings suggest that incircumstances with limited training data finetuning self-supervisedrepresentations is a better performing and viable solution.

Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching