We investigate the role of pre-linguistic normalization in the perception of US English vowels. We train Bayesian ideal observer (IO) models on unnormalized or normalized acoustic cues to vowel identity using a phonetic database of 8 /h-VOWEL-d/ words of US English. We then compare the IOs’ predictions for vowel categorization against L1 US English listeners’ 8-way categorization responses for recordings of /h-VOWEL-d/ words in a web-based experiment. Results indicate that pre-linguistic normalization substantially improves the fit to human responses from 74% to 90% of best-possible performance.