Multilingual self-supervised speech representations improve the speech
  recognition of low-resource African languages with codeswitching
Tolúlopé Ògúnrèmí Christopher D. Manning Dan Jurafsky
While many speakers of low-resource languages regularly code-switch between
their languages and other regional languages or English, datasets of
codeswitched speech are too small to train bespoke acoustic models from scratch
or do language model rescoring. Here we propose finetuning self-supervised
speech representations such as wav2vec 2.0 XLSR to recognize code-switched
data. We find that finetuning self-supervised multilingual representations and
augmenting them with n-gram language models trained from transcripts reduces
absolute word error rates by up to 20% compared to baselines of hybrid models
trained from scratch on code-switched data. Our findings suggest that in
circumstances with limited training data finetuning self-supervised
representations is a better performing and viable solution.