PhD A thought the results showed that delta features were important and that the TIMIT-trained network performed best across languages so far, though the reason was unclear. They observed that MLPs reduced error when trained on the target task, but could hurt performance on a different task when the input features were well normalized. MSG also added little for Italian, and PhD A thought its poor performance might have a simple cause that still needed investigation.