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Found 5 papers, 2 papers with code
Varianceflow: High-Quality and Controllable Text-to-Speech using Variance Information via Normalizing Flow
Also, the objective function of NF makes the model use the variance information and the text in a disentangled manner resulting in more precise variance control.
Avocodo: Generative Adversarial Network for Artifact-free Vocoder
Therefore, in this paper, we investigate the relationship between these artifacts and GAN-based vocoders and propose a GAN-based vocoder, called Avocodo, that allows the synthesis of high-fidelity speech with reduced artifacts.
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and Natural Non-Autoregressive Text-to-Speech
This paper proposes a hierarchical and multi-scale variational autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to generate natural speech with diverse speaking styles.
GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis
Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the generation of reasonably good speech quality with a single model and made it possible to synthesize the speech of a speaker with limited training data.
VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network
Additionally, compared with Parallel WaveGAN, another recently developed high-fidelity vocoder, VocGAN is 6. 98x faster on a CPU and exhibits higher MOS.
