PhD A thought the results showed that delta features were important and that the TIMIT-trained network performed best overall, though it was unclear why. MLPs reduced errors when trained on the target task, but could hurt performance on different tasks unless the input features were poorly normalized. MSG did not improve the Italian results, and PhD A thought a simple processing issue might be responsible.