Fine-grained evaluation of speech recognition results
You have misconception that errors are caused by either language model or by acoustic model. There is no clear distinction because models are combined together in during search for best hypothesis. Interaction is complex and another major player is beam search pruning.

Most errors are originated from the acoustic model when it fails to give proper estimate for a certain frame, but the problem is that they propagate over next few words. For example, if you have an unknown word, it will not be recognized properly but the next several words will be wrong too because search state will not match the actual content. And phone-by-phone comparison will not be really meaningful. 

So it's a bit more complex.