It's always been a puzzle to me that published WER is so low, and yet when I use dictation (which is a lot--I use it for almost all text messages) I always have to make numerous corrections.

This sentence is crucial and suggests a way to understand how WER can be apparently lower than human levels, yet ASR is still obviously imperfect:

> When comparing models to humans, it’s important to check the nature of the mistakes and not just look at the WER as a conclusive number. In my own experience, human transcribers tend to make fewer and less drastic semantic errors than speech recognizers.

This suggests that humans make mistakes specifically on semantically unimportant words, while computers make mistakes more uniformly. That is, humans are able to allocate resources to correct word identification for the important words, with less resources going to the less important ones. So maybe the way to improve speech recognition is not to focus on WER, but on WER weighted by word importance, or to train speech recognition systems end-to-end with some end goal language task so that the DNN or whatever learns to recognize the important words for the task.