標題: ADVERSARIAL MANIFOLD LEARNING FOR SPEAKER RECOGNITION
作者: Chien, Jen-Tzung
Peng, Kang-Ting
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Probabilistic linear discriminant analysis;adversarial learning;manifold learning;speaker recognition
公開日期: 1-Jan-2017
摘要: 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.
URI: http://hdl.handle.net/11536/146981
期刊: 2017 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU)
起始頁: 599
結束頁: 605
Appears in Collections:Conferences Paper