  Recent advances in self-supervised learning through contrastive training have
shown that it is possible to learn a competitive speech recognition system with
as little as 10 minutes of labeled data. However, these systems are
computationally expensive since they require pre-training followed by
fine-tuning in a large parameter space. We explore the performance of such
systems without fine-tuning by training a state-of-the-art speech recognizer on
the fixed representations from the computationally demanding wav2vec 2.0
framework. We find performance to decrease without fine-tuning and, in the
extreme low-resource setting, wav2vec 2.0 is inferior to its predecessor. In
addition, we find that wav2vec 2.0 representations live in a low dimensional
subspace and that decorrelating the features of the representations can
stabilize training of the automatic speech recognizer. Finally, we propose a
bidirectional extension to the original wav2vec framework that consistently
improves performance.
