While I'm not sure, I assume the author is referring to "error reduction". Error reduction is defined as def ER(accuracy_before, accuracy_after): d

While I'm not sure, I assume the author is referring to "error reduction". Error reduction is defined as def ER(accuracy_before, accuracy_after): delta = accuracy_after - accuracy_before return delta / (1. - accuracy_before) (It is often written as a percentage.) Note that ER(.5, .75) = ER(.98, .99) = .5. It is for this reason I dislike it. Without knowing what performance before and after was like, it's hard to tell whether this was an incremental improvement to an already impressive system or the innovation that made a system useable. (It strikes me as unlikely that the author means to say that Google reduced the Android recognizer's word error rate [WER] by 25%, since WERs on many publicly available databases used for evaluation purposes were already well below 25% before the deep learning revolution. But, it is not impossible that Google's test set was particularly hard and the pre-deep-learning Android recognizer was unimpressive. Caveat: I know, I know, WER isn't really "accuracy".)