  Unsupervised speech representation learning has shown remarkable success at
finding representations that correlate with phonetic structures and improve
downstream speech recognition performance. However, most research has been
focused on evaluating the representations in terms of their ability to improve
the performance of speech recognition systems on read English (e.g. Wall Street
Journal and LibriSpeech). This evaluation methodology overlooks two important
desiderata that speech representations should have: robustness to domain shifts
and transferability to other languages. In this paper we learn representations
from up to 8000 hours of diverse and noisy speech data and evaluate the
representations by looking at their robustness to domain shifts and their
ability to improve recognition performance in many languages. We find that our
representations confer significant robustness advantages to the resulting
recognition systems: we see significant improvements in out-of-domain transfer
relative to baseline feature sets and the features likewise provide
improvements in 25 phonetically diverse languages including tonal languages and
low-resource languages.
