Neural Discriminant Analysis for Deep Speaker Embedding
Lantian Li, Dong Wang, Thomas Fang Zheng · 2020
Probabilistic Linear Discriminant Analysis (PLDA) is a popular tool in open-set classification/verification tasks.However, the Gaussian assumption underlying PLDA prevents it from being applied to situations where the data is clearly non-Gaussian.In this paper, we present a novel nonlinear version of PLDA named as Neural Discriminant Analysis (NDA).This model employs an invertible deep neural network to transform a complex distribution to a simple Gaussian, so that the linear Gaussian model can be readily established in the transformed space.We tested this NDA model on a speaker recognition task where the deep speaker vectors (x-vectors) are presumably non-Gaussian.Experimental results on two datasets demonstrate that NDA consistently outperforms PLDA, by handling the non-Gaussian distributions of the x-vectors.