  Speech synthesis is widely used in many practical applications. In recent
years, speech synthesis technology has developed rapidly. However, one of the
reasons why synthetic speech is unnatural is that it often has over-smoothness.
In order to improve the naturalness of synthetic speech, we first extract the
mel-spectrogram of speech and convert it into a real image, then take the
over-smooth mel-spectrogram image as input, and use image-to-image translation
Generative Adversarial Networks(GANs) framework to generate a more realistic
mel-spectrogram. Finally, the results show that this method greatly reduces the
over-smoothness of synthesized speech and is more close to the mel-spectrogram
of real speech.
