A model is presented that explains perceptual compensation for context as a consequence of listeners optimally categorizing speech sounds given contextual variation. In using Bayes' rule to pick the most likely category, listeners' perception of speech sounds, which is biased toward the means of phonetic categories (Feldman & Griffiths, 2007; Feldman, Griffiths, & Morgan, 2009), is conditioned by contextual variation. The effect on the resulting identification curves of varying category frequencies and variances is discussed. A simulation case study of compensation for vowel-to-vowel coarticulation shows the predictions of the model closely correspond to human perceptual data.