Reduction of dimension of HMM parameters using ICA and PCA in MLLR framework for speaker adaptation

We discuss how to reduce the number of inverse matrix and its dimensions requested in MLLR framework for speaker adaptation. To find a smaller set of variables with less redundancy, we employ PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible. The amount of additional computation when PCA or ICA is applied is as small as it can be disregarded. The dimension of HMM parameters is reduced to about 1/3 ~ 2/7 dimensions of SI(speaker independent) model parameter with which speech recognition system represents word recognition rate as much as ordinary MLLR framework. If dimension of SI model parameter is n , the amount of computation of inverse matrix in MLLR is proportioned to ) ( 4 n O . So, compared with ordinary MLLR, the amount of total computation requested in speaker adaptation is reduced to about 1/80 ~ 1/150.