We propose a novel way to alleviate the data sparseness problem in training letter-to-sound (LTS) N-gram models by adding automatically generated new words to the training set. The proposed method consists of two procedures: (1) generating a large pool of new words automatically; (2) selecting good new word candidates from the new word pool via semi-supervised learning. The new words are created by replacing stressed syllables of an existing word with other stressed syllables under specified contextual constraints. The new word selection by semi-supervised learning is based upon consistent pronunciation predictions by different LTS models. After adding new words to the training set, the performance of LTS conversion is significantly improved. For the NetTalk dictionary, compared with the performance from the N-gram baseline model, 21.6% relative word error rate reduction is obtained. For the CMU dictionary, 9.1% and 5.6% relative word error rate reductions are obtained, respectively, with/without considering the stress.