Data Augmentation Using Variational Autoencoder for Embedding Based Speaker Verification

Domain or environment mismatch between training and testing, such as various noises and channels, is a major challenge for speaker verification. In this paper, a variational autoencoder (VAE) is designed to learn the patterns of speaker embeddings extracted from noisy speech segments, including i-vector and x-vector, and generate embeddings with more diversity to improve the robustness of speaker verification systems with probabilistic linear discriminant analysis (PLDA) back-end. The approach is evaluated on the standard NIST SRE 2016 dataset. Compared to manual and generative adversarial network (GAN) based augmentation approaches, the proposed VAE based augmentation achieves a slightly better performance for i-vector on Tagalog and Cantonese with EERs of 15.54% and 7.84%, and a more significant improvement for x-vector on those two languages with EERs of 11.86% and 4.20%.