標題: VARIATIONAL MANIFOLD LEARNING FOR SPEAKER RECOGNITION
作者: Chien, Jen-Tzung
Hsu, Cheng-Wei
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Probabilistic linear discriminant analysis;deep learning;variational manifold learning;speaker recognition
公開日期: 1-Jan-2017
摘要: This paper presents a variational manifold learning for speaker recognition based on the probabilistic linear discriminant analysis (PLDA) using i-vectors. A latent variable model is introduced to compensate the constraints of the linearity in PLDA scoring and the high dimensionality in using i-vectors. A deep variational learning is formulated to jointly optimize three objectives including a regularization for variational distributions, a reconstruction based on PLDA and a manifold learning for neighbor embedding. A stochastic gradient variational Bayesian algorithm is developed to optimize the variational lower bound of log likelihood where the expectation in the objectives is estimated via a sampling method. Interestingly, the latent variables in the proposed variational manifold PLDA (vm-PLDA) are capable of decoding or reconstructing the i-vectors. The experiments on visualization and speaker recognition show the merits of vm-PLDA in manifold learning and classification.
URI: http://hdl.handle.net/11536/146832
ISSN: 1520-6149
期刊: 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
起始頁: 4935
結束頁: 4939
Appears in Collections:Conferences Paper