  Recent research in speech processing exhibits a growing interest in
unsupervised and self-supervised representation learning from unlabelled data
to alleviate the need for large amounts of annotated data. We investigate
several popular pre-training methods and apply them to Flemish Dutch. We
compare off-the-shelf English pre-trained models to models trained on an
increasing amount of Flemish data. We find that the most important factors for
positive transfer to downstream speech recognition tasks include a substantial
amount of data and a matching pre-training domain. Ideally, we also finetune on
an annotated subset in the target language. All pre-trained models improve
linear phone separability in Flemish, but not all methods improve Automatic
Speech Recognition. We experience superior performance with wav2vec 2.0 and we
obtain a 30% WER improvement by finetuning the multilingually pre-trained
XLSR-53 model on Flemish Dutch, after integration into an HMM-DNN acoustic
model.
