  End-to-end Automatic Speech Recognition (ASR) models are usually trained to
optimize the loss of the whole token sequence, while neglecting explicit
phonemic-granularity supervision. This could result in recognition errors due
to similar-phoneme confusion or phoneme reduction. To alleviate this problem,
we propose a novel framework based on Supervised Contrastive Learning (SCaLa)
to enhance phonemic representation learning for end-to-end ASR systems.
Specifically, we extend the self-supervised Masked Contrastive Predictive
Coding (MCPC) to a fully-supervised setting, where the supervision is applied
in the following way. First, SCaLa masks variable-length encoder features
according to phoneme boundaries given phoneme forced-alignment extracted from a
pre-trained acoustic model; it then predicts the masked features via
contrastive learning. The forced-alignment can provide phoneme labels to
mitigate the noise introduced by positive-negative pairs in self-supervised
MCPC. Experiments on reading and spontaneous speech datasets show that our
proposed approach achieves 2.8 and 1.4 points Character Error Rate (CER)
absolute reductions compared to the baseline, respectively.
