Firstly, I would create $tf from my $tf = Text::Fuzzy->new ($stringToMatch); outside the inner loop. There is no need to re-create that object for the same base word ($stringToMatch) over and over each item in the corpus. Since the number of stringsToMatch is much less than Corpus, it is a good thing (for this suggestion) that the loops are arranged the way they are.

If you are going to repeat above process for different @stringsToMatch but the same @Corpus and you have RAM to spare then try creating one Text::Fuzzy object for each corpus element ONCE and cache these for each subsequent run.

Another option is before calling $tf->distance(), which I assume it is expensive, to have a few relatively cheap heuristics of your own to decide whether it is going to be a waste of time to call the expensive distance(). You are already using one by checking on the number of words in each string.

If the distance function's performance is worse than linear to the length of strings then maybe break your strings to words and use the mean or max of their distances. Fewer letters => faster even if more words.

If the distance algorithm you are using is too expensive then experiment with converting your words to phonetic representation using one of the algorithms in Text::Phonetic. That could accommodate for different spellings at least (possibly).

And of course there is the route of AI and neural networks. But I need to dust these off.

p.s. With heuristics you must keep statistics of their performance in order to assess them later.

bw, bliako