I wrote a short Python script which cross-references the W3W word list with a homophones list[1], and found this: census,senses choral,coral clairv

I wrote a short Python script which cross-references the W3W word list with a homophones list[1], and found this: census,senses choral,coral clairvoyance,clairvoyants collard,collared confectionary,confectionery disburse,disperse epic,epoch equivalence,equivalents hurdle,hurtle incidence,incidents incite,insight incompetence,incompetents independence,independents innocence,innocents instance,instants intense,intents lightening,lightning ordinance,ordnance overdo,overdue parse,pass pokey,poky precedence,precedents,presidents purest,purist recede,reseed sari,sorry senses,census variance,variants verses,versus Those aren't sound-alikes, they're straight-up homophones. If we cross-referenced against a list which contained sound-alikes, I'm sure we'd end up with a much larger list of collisions. EDIT: This is the script: with open('word_data.txt') as f: words = set(l.strip() for l in f.readlines()) with open('homophones.txt') as f: for l in f.readlines(): l = l.strip() homophone_group = (h.strip('*') for h in l.split()) filtered_homophone_group = tuple(h for h in homophone_group if h in words) if len(filtered_homophone_group) > 1: print(','.join(filtered_homophone_group)) The `strip('*')` bit is needed because my copy/paste into homophones.txt pulled in some asterices(sp?) and I didn't want to go through and fix them manually. [1] http://homophonelist.com/homophones-list/ http://homophonelist.com/homophones-list/