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Multiple-pronunciation dictionaries are often used by automatic speech recognition systems in order to account for different speaking styles. In this paper, two methods based on statistical machine translation (SMT) are used to generate multiple pronunciations from the canonical pronunciation of a word. In the first method, a machine translation tool is used to perform phoneme-to-phoneme (p2p) conversion and derive variants from a given canonical pronunciation. The second method is based on a pivot method proposed for the paraphrase extraction task. The two methods are compared under different training conditions which allow single or multiple pronunciations in the training set, and their performance is evaluated in terms of recall and precision measures.