  Recent progress in self-training, self-supervised pretraining and
unsupervised learning enabled well performing speech recognition systems
without any labeled data. However, in many cases there is labeled data
available for related languages which is not utilized by these methods. This
paper extends previous work on zero-shot cross-lingual transfer learning by
fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen
languages. This is done by mapping phonemes of the training languages to the
target language using articulatory features. Experiments show that this simple
method significantly outperforms prior work which introduced task-specific
architectures and used only part of a monolingually pretrained model.
