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Recently, the effectiveness of text-to-speech (TTS) systems combined withneural vocoders to generate high-fidelity speech has been shown. However,collecting the required training data and building these advanced systems fromscratch are time and resource consuming. An economical approach is to develop aneural vocoder to enhance the speech generated by existing or low-cost TTSsystems. Nonetheless, this approach usually suffers from two issues: 1)temporal mismatches between TTS and natural waveforms and 2) acousticmismatches between training and testing data. To address these issues, we adopta cyclic voice conversion (VC) model to generate temporally matched pseudo-VCdata for training and acoustically matched enhanced data for testing the neuralvocoders. Because of the generality, this framework can be applied to arbitraryTTS systems and neural vocoders. In this paper, we apply the proposed methodwith a state-of-the-art WaveNet vocoder for two different basic TTS systems,and both objective and subjective experimental results confirm theeffectiveness of the proposed framework.

A Cyclical Post-filtering Approach to Mismatch Refinement of Neural Vocoder for Text-to-speech Systems