Improving phoneme and accent estimation by leveraging a dictionary for a stochastic TTS front-end

Determining the correct phonemes and pitch accents is important for creating natural Japanese speech. We implemented a TTS front-end system based on an n-gram model. However, the vocabulary of the word n-gram model is limited to the list of the words found in the training corpus, and collecting a very large training corpus is not an easy task. In this paper, we propose using an additional class n-gram model to incorporate not only the words found in the training corpus, but the words found in the dictionary to further improve the accuracy. In our experiments, our proposed model relatively improves the accuracy for estimating accents by 16.9% and the accuracy for estimating phonemes by 21.6% compared to the word n-gram model.