  In this work, we investigate if the wav2vec 2.0 self-supervised pretraining
helps mitigate the overfitting issues with connectionist temporal
classification (CTC) training to reduce its performance gap with flat-start
lattice-free MMI (E2E-LFMMI) for automatic speech recognition with limited
training data. Towards that objective, we use the pretrained wav2vec 2.0 BASE
model and fine-tune it on three different datasets including out-of-domain
(Switchboard) and cross-lingual (Babel) scenarios. Our results show that for
supervised adaptation of the wav2vec 2.0 model, both E2E-LFMMI and CTC achieve
similar results; significantly outperforming the baselines trained only with
supervised data. Fine-tuning the wav2vec 2.0 model with E2E-LFMMI and CTC we
obtain the following relative WER improvements over the supervised baseline
trained with E2E-LFMMI. We get relative improvements of 40% and 44% on the
clean-set and 64% and 58% on the test set of Librispeech (100h) respectively.
On Switchboard (300h) we obtain relative improvements of 33% and 35%
respectively. Finally, for Babel languages, we obtain relative improvements of
26% and 23% on Swahili (38h) and 18% and 17% on Tagalog (84h) respectively.
