We use sequence-to-sequence networks trained on sequential phonetic encoding tasks to construct compositional phonological representations of words. We show that the output of an encoder network can predict the phonetic durations of American English words better than a number of alternative forms. We also show that the model’s learned representations map onto existing measures of words’ phonological structure (phonological neighborhood density and phonotactic probability).