  Pre-trained acoustic representations such as wav2vec and DeCoAR have attained
impressive word error rates (WER) for speech recognition benchmarks,
particularly when labeled data is limited. But little is known about what
phonetic properties these various representations acquire, and how well they
encode transferable features of speech. We compare features from two
conventional and four pre-trained systems in some simple frame-level phonetic
classification tasks, with classifiers trained on features from one version of
the TIMIT dataset and tested on features from another. All contextualized
representations offered some level of transferability across domains, and
models pre-trained on more audio data give better results; but overall, DeCoAR,
the system with the simplest architecture, performs best. This type of
benchmarking analysis can thus uncover relative strengths of various proposed
acoustic representations.
