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To simplify the generation process, several text-to-speech (TTS) systemsimplicitly learn intermediate latent representations instead of relying onpredefined features (e.g., mel-spectrogram). However, their generation qualityis unsatisfactory as these representations lack speech variances. In thispaper, we improve TTS performance by adding \emph{prosody embeddings} to thelatent representations. During training, we extract reference prosodyembeddings from mel-spectrograms, and during inference, we estimate theseembeddings from text using generative adversarial networks (GANs). Using GANs,we reliably estimate the prosody embeddings in a fast way, which have complexdistributions due to the dynamic nature of speech. We also show that theprosody embeddings work as efficient features for learning a robust alignmentbetween text and acoustic features. Our proposed model surpasses severalpublicly available models with less parameters and computational complexity incomparative experiments.

Adversarial Learning of Intermediate Acoustic Feature for End-to-End Lightweight Text-to-Speech