We present and experimentally evaluate a new model of prounciation by analogy: the paradigmatic cascades model. Given a pronunciation lexicon, this algorithm first extracts the most productive paradigmatic mappings in the graphemic domain, and pairs them statistically with their correlate(s) in the phonemic domain. These mappings are used to search and retrieve in the lexical database the most promising analog of unseen words. We finally apply to the analogs pronunciation the correlated series of mappings in the phonemic domain to get the desired pronunciation.