Neural adversarial learning for speaker recognition. (November 2019) - Chien, Jen-Tzung; Peng, Kang-Ting
Highlights: A series of neural adversarial learning methods are proposed for speaker recognition. Deep learning is implemented for PLDA speaker recognition with i -vectors. Adversarial manifold learning is proposed to build a subspace model with neighbor embeddings in latent variables. Multiobjective learning is performed for minimax optimization. Adversarial augmentation learning is proposed to compensate the imbalanced data problem in speaker recognition. Abstract: This paper presents the adversarial learning approaches to deal with various tasks in speaker recognition based on probabilistic discriminant analysis (PLDA) which is seen as a latent variable model for reconstruction of i -vectors. The first task aims to reduce the dimension of i -vectors based on an adversarial manifold learning where the adversarial neural networks of generator and discriminator are merged to preserve neighbor embedding of i -vectors in a low-dimensional space. The generator is trained to fool the discriminator with the generated samples in latent space. A PLDA subspace model is constructed by jointly minimizing a PLDA reconstruction error, a manifold loss for neighbor embedding and an adversarial loss caused by the generator and discriminator. The second task of adversarial learning is developed to tackle the imbalanced data problem. A PLDA based generative adversarial network is trained to generate new i -vectors to balance the size of training utterances across different speakers. An adversarial augmentation learning is proposed for robust speaker recognition. In particular, the minimax optimization is performed to estimate a generator and a discriminator where the class conditional i -vectors produced by generator could not be distinguished from real i -vectors via discriminator. A multiobjective learning is realized for a specialized neural model with the cosine similarity between real and fake i -vectors as well as the regularization for Gaussianity. Experiments are conducted to show the merit of adversarial learning in subspace construction and data augmentation for PLDA-based speaker recognition.