L-Mix: A Latent-Level Instance Mixup Regularization for Robust Self-Supervised Speaker Representation Learning

Over the recent years, various self-supervised embedding learning methods for deep speaker verification were proposed. The performance of the self-supervised learning framework highly depends on the data augmentation technique, but due to the sensitive nature of speaker information within the speech signal, most speaker embedding training relies on simple augmentations such as additive noise or simulated reverberation. Thus while the conventional self-supervised speaker embedding systems can yield minimum within-utterance variability, their capability to generalize to out-of-set utterance is limited. In order to alleviate this problem, we investigate the utilization of the instance mix (i-mix) regularization for training a self-supervised speaker embedding system. Moreover, we propose a new mixup strategy that applies i-mix on the latent space, instead of the raw acoustic feature domain. In this paper, the i-mix and the proposed l-mix strategy were incorporated into the self-supervised angular prototypical and softmax-based objective functions and were evaluated on the VoxCeleb dataset. From the experimental results, we observe that the self-supervised embedding network can benefit greatly from the i-mix and l-mix strategies in terms of training stability and speaker verification performance.