Multi-layer structure MLLR adaptation algorithm based on subspace regression classes

In many adaptation algorithms were proposed in the last decade, most notable MAP estimation and MLLR transformation. When the amount of adaptation data is limited, adaptation can be done by grouping similar Gaussians together to form regression classes and then transforming the Gaussians in groups. We propose a rapid MLLR adaptation algorithm with multiply layer structure, which is called SRCMLR. The method groups the Gaussians at a finer acoustic subspace level, which is constructed on the target driven. It generates the regression class dynamically for each subspace, basing on the outcome of the former MLLR transformation. Because of the new algorithm´s special transformation structure and cluster space, there are fewer parameters to estimate for the subsequent MLLR transformation matrix, so computation load in performing transformation is much reduced. Experiments show that the use of SRCMLLR is more effective than other methods when the adaptation data is scare.