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While generative adversarial networks (GANs) based neural text-to-speech(TTS) systems have shown significant improvement in neural speech synthesis,there is no TTS system to learn to synthesize speech from text sequences withonly adversarial feedback. Because adversarial feedback alone is not sufficientto train the generator, current models still require the reconstruction losscompared with the ground-truth and the generated mel-spectrogram directly. Inthis paper, we present Multi-SpectroGAN (MSG), which can train themulti-speaker model with only the adversarial feedback by conditioning aself-supervised hidden representation of the generator to a conditionaldiscriminator. This leads to better guidance for generator training. Moreover,we also propose adversarial style combination (ASC) for better generalizationin the unseen speaking style and transcript, which can learn latentrepresentations of the combined style embedding from multiple mel-spectrograms.Trained with ASC and feature matching, the MSG synthesizes a high-diversitymel-spectrogram by controlling and mixing the individual speaking styles (e.g.,duration, pitch, and energy). The result shows that the MSG synthesizes ahigh-fidelity mel-spectrogram, which has almost the same naturalness MOS scoreas the ground-truth mel-spectrogram.

Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis