Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification
This addresses domain mismatch issues in speaker verification, offering an end-to-end solution that simplifies scoring, though it is incremental as it builds on existing GAN methods.
The paper tackles domain robustness in speaker verification by using Generative Adversarial Networks to learn domain-invariant speaker embeddings, achieving a 7.2% relative improvement over a baseline system on the NIST-SRE 2016 dataset.
This article presents a novel approach for learning domain-invariant speaker embeddings using Generative Adversarial Networks. The main idea is to confuse a domain discriminator so that is can't tell if embeddings are from the source or target domains. We train several GAN variants using our proposed framework and apply them to the speaker verification task. On the challenging NIST-SRE 2016 dataset, we are able to match the performance of a strong baseline x-vector system. In contrast to the the baseline systems which are dependent on dimensionality reduction (LDA) and an external classifier (PLDA), our proposed speaker embeddings can be scored using simple cosine distance. This is achieved by optimizing our models end-to-end, using an angular margin loss function. Furthermore, we are able to significantly boost verification performance by averaging our different GAN models at the score level, achieving a relative improvement of 7.2% over the baseline.