A Multi-Scale Time-Frequency Spectrogram Discriminator for GAN-based Non-Autoregressive TTS

Understand

The generative adversarial network (GAN) has shown its outstanding capability in improving Non-Autoregressive TTS (NAR-TTS) by adversarially training it with an extra model that discriminates between the real and the generated speech.

• To maximize the benefits of GAN, it is crucial to find a powerful discriminator that can capture rich distinguishable information.
• In this paper, we propose a multi-scale time-frequency spectrogram discriminator to help NAR-TTS generate high-fidelity Mel-spectrograms.
• It treats the spectrogram as a 2D image to exploit the correlation among different components in the time-frequency domain.