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Current two-stage TTS framework typically integrates an acoustic model with avocoder -- the acoustic model predicts a low resolution intermediaterepresentation such as Mel-spectrum while the vocoder generates waveform fromthe intermediate representation. Although the intermediate representation isserved as a bridge, there still exists critical mismatch between the acousticmodel and the vocoder as they are commonly separately learned and work ondifferent distributions of representation, leading to inevitable artifacts inthe synthesized speech. In this work, different from using pre-designedintermediate representation in most previous studies, we propose to use VAEcombining with GAN to learn a latent representation directly from speech andthen utilize a flow-based acoustic model to model the distribution of thelatent representation from text. In this way, the mismatch problem is migratedas the two stages work on the same distribution. Results demonstrate that theflow-based acoustic model can exactly model the distribution of our learnedspeech representation and the proposed TTS framework, namely Glow-WaveGAN, canproduce high fidelity speech outperforming the state-of-the-art GAN-basedmodel.

Glow-WaveGAN: Learning Speech Representations from GAN-based Variational Auto-Encoder For High Fidelity Flow-based Speech Synthesis