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 [8], 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.