Search Results for author:
Found 4 papers, 2 papers with code
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training
In this paper, we propose InferGrad, a diffusion model for vocoder that incorporates inference process into training, to reduce the inference iterations while maintaining high generation quality.
Cross-speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis
Secondly, in these models the content/text, prosody, and speaker timbre are usually highly entangled, it's therefore not realistic to expect a satisfied result when freely combining these components, such as to transfer speaking style between speakers.
Forward-Backward Decoding for Regularizing End-to-End TTS
Experimental results show our proposed methods especially the second one (bidirectional decoder regularization), leads a significantly improvement on both robustness and overall naturalness, as outperforming baseline (the revised version of Tacotron2) with a MOS gap of 0. 14 in a challenging test, and achieving close to human quality (4. 42 vs. 4. 49 in MOS) on general test.
Learning latent representations for style control and transfer in end-to-end speech synthesis
In this paper, we introduce the Variational Autoencoder (VAE) to an end-to-end speech synthesis model, to learn the latent representation of speaking styles in an unsupervised manner.
