  Self-supervised learning (SSL) has shown promise in learning representations
of audio that are useful for automatic speech recognition (ASR). But, training
SSL models like wav2vec~2.0 requires a two-stage pipeline. In this paper we
demonstrate a single-stage training of ASR models that can utilize both
unlabeled and labeled data. During training, we alternately minimize two
losses: an unsupervised masked Contrastive Predictive Coding (CPC) loss and the
supervised audio-to-text alignment loss Connectionist Temporal Classification
(CTC). We show that this joint training method directly optimizes performance
for the downstream ASR task using unsupervised data while achieving similar
word error rates to wav2vec~2.0 on the Librispeech 100-hour dataset. Finally,
we postulate that solving the contrastive task is a regularization for the
supervised CTC loss.
