Speaker Adaptation Based on PPCA of Acoustic Models in a Two-Way Array Representation
Yongwon Jeong · IEICE Transactions on Information and Systems · 2014
We propose a speaker adaptation method based on the probabilistic principal component analysis (PPCA) of acoustic models. We define a training matrix which is represented in a two-way array and decompose the training models by PPCA to construct bases. In the two-way array representation, each training model is represented as a matrix and the columns of each training matrix are treated as training vectors. We formulate the adaptation equation in the maximum a posteriori (MAP) framework using the bases and the prior.