Block-wise training for i-vector
Fanhu Bie, Jun Wang, Dong Wang, Thomas Fang Zheng · 2014
We propose a fast block-wise and parallel training approach to train i-vector systems. This approach divides the loading matrix into groups according to components or acoustic feature dimensions and trains the loading matrices of these groups independently and in parallel. These individually trained block matrices can be combined to approximate the original loading matrix, or used to derive independent i-vectors. We tested the block-wise training on speaker verification tasks based on the NIST SRE data and found that it can substantially speed up the training while retaining the quality of the resulting i-vectors.