Improvements on minimum covariance based Spatial correlation Transformation

In order to take advantage of the correlation information among different acoustic units in speech recognition, a novel approach named Minimum Covariance based Spatial Correlation Transformation was proposed in, which achieves satisfactory performance. However, there are two issues of this approach which can still be improved, 1) the estimation of the transformation matrix; 2) the construction of the history data. In this paper, a new algorithm of estimating the transformation matrix and a new strategy of constructing history supervector are proposed. Experimental results show that the improved approach achieves better performance than the original one.