Neural Text-to-Speech Generative Adversarial Network

This paper introduces a novel approach to text-to-speech synthesis using a Generative Adversarial Network (GAN). The model, called Neural TTS-GAN, significantly improves the naturalness and quality of synthesized speech by training a generator to produce speech waveforms from text and a discriminator to distinguish between real and synthesized speech. This adversarial training process results in more realistic and human-like speech synthesis compared to traditional methods.