I'm trying to model a phonetic recognizer that has to isolate instances of words (strings of phones) out of a long stream of phones that doesn't have gaps between each word. The stream of phones may have been poorly recognized, with letter substitutions/insertions/deletions, so I will have to do approximate string matching.

However, I want the matching to be phonetically-motivated, e.g. "m" and "n" are phonetically similar, so the substitution cost of "m" for "n" should be small, compared to say, "m" and "k". So, if I'm searching for [mein] "main", it would match the letter sequence [meim] "maim" with, say, cost 0.1, whereas it would match the letter sequence [meik] "make" with, say, cost 0.7. Similarly, there are differing costs for inserting or deleting each letter. I can supply a confusion matrix that, for each letter pair (x,y), gives the cost of substituting x with y, where x and y are any letter or the empty string.

I know that there are tools available that do approximate matching such as agrep, but as far as I can tell, they do not take a confusion matrix as input. That is, the cost of any insertion/substitution/deletion = 1. My question is, are there any open-source tools already available that can do approximate matching with confusion matrices, and if not, what is a good algorithm that I can implement to accomplish this?