3 accepted papers
In this article we propose a novel approach for adapting speaker embeddings to new domains based on adversarial training of neural networks. We apply our embeddings to the task of text-independent speaker verification, a challenging, real-world problem in biometric security. We further the developme…
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 it cannot tell if embeddings are from the source or target domains. We train several GAN variants using our propos…
This paper introduces a new formulation of Joint Factor Analysis (JFA) for text-dependent speaker recognition based on left-to-right modeling with tied mixture HMMs. It accommodates many different ways of extracting multiple features to characterize speakers (features may or may not be HMM state-dep…