​​Contrastive Semi-supervised Learning for ASR

Nowadays, pseudo-labeling is the most common method for pre-training automatic speech recognition (ASR) models, but in the case of low-resource setups and domain transfer, it suffers from a supervised teacher model’s degrading quality. The authors of this paper suggest using contrastive learning to overcome this problem.

CSL approach (Contrastive Semi-supervised Learning) uses teacher-generated predictions to select positive and negative examples instead of using pseudo-labels directly.

Experiments show that CSL has lower WER not only in comparison with standard CE-PL (Cross-Entropy pseudo-labeling) but also under low-resource and out-of-domain conditions.

To demonstrate its resilience to pseudo-labeling noise, the authors apply CSL pre-training in a low-resource setup with only 10hr of labeled data, where it reduces WER by 8% compared to the standard cross-entropy pseudo-labeling (CE-PL). This WER reduction increase to 19% with a teacher trained only on 1hr of labels and 17% for out-of-domain conditions.


Paper: https://arxiv.org/abs/2103.05149

#deeplearning #asr #contrastivelearning #semisupervised