PhD A thought the results showed that delta features were important and that the TIMIT-trained network performed best across languages so far. They also observed that target-trained networks reduced error, while networks trained on different tasks could hurt performance when features were well normalized. They were unsure how much of the difference came from language, noise, or the quality of the training labels, and thought more results were needed.