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: | 1-一月-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 |
顯示於類別: | 會議論文 |