Feature and model space speaker adaptation with full covariance Gaussians

Full covariance models can give better results for speech recognition than diagonal models, yet they introduce complications for standard speaker adaptation techniques such as MLLR and fMLLR. Here we introduce efficient update methods to train adaptation matrices for the full covariance case. We also experiment with a simplified technique in which we pretend that the full covariance Gaussians are diagonal and obtain adaptation matrices under that assumption. We show that this approximate method works almost as well as the exact method.