Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis. (arXiv:2211.01964v1 [cs.CL])

Embedding paralinguistic properties is a challenging task as there are only a
few hours of training data available for domains such as emotional speech. One
solution to this problem is to pretrain a general self-supervised speech
representation model on large amounts of unlabeled speech. This pretrained
model is then finetuned to a specific task. Paralinguistic properties however
have notoriously high class variance, making the finetuning ineffective. In
this work, we propose a two step approach to this. First we improve …

analysis arxiv fine-tuning losses speech speech analysis