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 the
embedding space, then we train an adapter to bridge the gap from the embedding
space to a classification task. In order to improve the class invariance we use
a combination of contrastive and non-contrastive losses to explicitly optimize
for class invariant, yet discriminative features. Our approach consistently
outperforms baselines that are finetuned end-to-end on multiple tasks and
surpasses a benchmark on state-of-the-art emotion classification.

本研究提出了一种改进内部嵌入空间和使用对比和非对比损失的适配器来处理类内差异的方法，并将其应用于情感分类问题，发现本方法在多个任务上均优于端到端微调基线，并超过最先进的情感分类基准。