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End-to-end speech synthesis is a promising approach that directly convertsraw text to speech. Although it was shown that Tacotron2 outperforms classicalpipeline systems with regards to naturalness in English, its applicability toother languages is still unknown. Japanese could be one of the most difficultlanguages for which to achieve end-to-end speech synthesis, largely due to itscharacter diversity and pitch accents. Therefore, state-of-the-art systems arestill based on a traditional pipeline framework that requires a separate textanalyzer and duration model. Towards end-to-end Japanese speech synthesis, weextend Tacotron to systems with self-attention to capture long-termdependencies related to pitch accents and compare their audio quality withclassical pipeline systems under various conditions to show their pros andcons. In a large-scale listening test, we investigated the impacts of thepresence of accentual-type labels, the use of force or predicted alignments,and acoustic features used as local condition parameters of the Wavenetvocoder. Our results reveal that although the proposed systems still do notmatch the quality of a top-line pipeline system for Japanese, we show importantstepping stones towards end-to-end Japanese speech synthesis.

Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language