I am trying to come up with a way using Python to find phonetic similarities between how differently written names with different meanings in different languages might sound alike. Names can be multiple words, even just acronyms, but not full sentences. There can be numbers and special characters as well in the names. The ultimate goal is to have a database of names, and compare one new name with the ones in the database and see which ones are more similar.
So far I tried:
Creating phonemes of the word with Phonetisaurus G2P with using the models here. Also with Allosaurus. I think the error rate is a bit high, but the main problem is I am not sure about how to compare the generated IPA phonemes because common distance algorithms such as Levenshtein do not consider the similar phonetic features of the phonemes, it just considers if they are the same character or not. I tried finding a table to see the similarities of IPA characters to map the values myself, but the IPA characters used in different projects are never all the same.
Generating audio files of each name according to its language with TTS and then extracting the features of the audio with libraries such as Librosa or SpeechPy. I believe this approach has potential, however, I am not sure about which features of the audio I should extract to accurately compare the way the words are heard, and also with what kind of a formula to calculate the general similarity using these features. Just taking the average didn't feel very ensuring.
I am checking a library called LingPy at the moment, but I couldn't figure out if and how I can use the options in it for what I am trying to do.
Even though there are many academic papers about comparing phonetic similarity of languages in general and audio processing, I couldn't find something that directly applies to what I am trying to do.
I would appreciate any resource that you can share about this.