Browsing by Author "Peng, Kang-Ting"

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ADVERSARIAL MANIFOLD LEARNING FOR SPEAKER RECOGNITION
(2017-01-01)
Chien, Jen-Tzung
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Peng, Kang-Ting
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電機工程學系
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Department of Electrical and Computer Engineering
This paper presents an adversarial manifold learning (AML) for speaker recognition based on the probabilistic linear discriminant analysis (PLDA) using i-vectors. PLDA basically consists of an encoder for finding the latent variables and a decoder for reconstructing the i-vectors. AML is developed and incorporated in deep learning for a latent variable model. Low-dimensional latent space is therefore constructed according to an adversarial learning with neighbor embedding. This AML-PLDA is formulated to jointly optimize three learning objectives including a reconstruction error based on PLDA, a subspace learning for neighbor embedding and an adversarial loss caused by a discriminator and a generator. Using the deep neural networks, the generator is trained to fool the discriminator with its generated samples in latent space. The parameters in encoder, decoder and discriminator are jointly estimated by using the stochastic gradient descent algorithm. The experiments on speaker recognition show the merit of AML-PLDA in manifold learning and pattern classification.
Neural adversarial learning for speaker recognition
(2019-11-01)
Chien, Jen-Tzung
;
Peng, Kang-Ting
;
電機工程學系
;
Department of Electrical and Computer Engineering
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. (C) 2019 Elsevier Ltd. All rights reserved.