New Speaker Adaptation Method Using 2-D PCA

This letter describes a speaker adaptation method based on the two-dimensional PCA of training models. In the method, state and dimension of mean vectors are differentiated, and the covariance matrix is computed dimension-wisely. As a result, the speaker weight can contain different weighting for each dimension of mean vectors. In the isolated-word recognition experiments, the proposed method performed better than both eigenvoice and MLLR, for adaptation data longer than about 15 seconds, due to its more elaborate modeling. The method can also be applied to other PCA-based modeling methods where each training model can be represented as a matrix.