Fine-grained evaluation of speech recognition results I know that usually ASR systems are evaluated using WER (word error rate), and I was wondering if it makes sense to use a more fine grained approach. E.g. transforming both the ASR hypothesis and the reference transcription to its phonetic representation (e.g. using an algorithm such as soundex or metaphone), and then taking the WER.  

Some half-baked ideas for use cases:


perhaps this evaluation method can help to distinguish between errors that stem from a bad language model to those that stem from a bad acoustic model
when using third-party ASR APIs, where the there's no control over the language model, perhaps it can be used to pick the best hypothesis among the top results (especially when the speech contains non-standard names and other out-of-vocabulary) words
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.