2 citations · 5 across the 3 of their papers we have counts for
4 papers
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training
Zehua Chen, Xu Tan, Ke Wang +4
Denoising diffusion probabilistic models (diffusion models for short) require a large number of iterations in inference to achieve the generation quality that matches or surpasses…
Cross-speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis
Shifeng Pan, Lei He
Cross-speaker style transfer is crucial to the applications of multi-style and expressive speech synthesis at scale. It does not require the target speakers to be experts in expres…
Forward-Backward Decoding for Regularizing End-to-End TTS
Yibin Zheng, Xi Wang, Lei He +4
Neural end-to-end TTS can generate very high-quality synthesized speech, and even close to human recording within similar domain text. However, it performs unsatisfactory when scal…
Learning latent representations for style control and transfer in end-to-end speech synthesis
Ya-Jie Zhang, Shifeng Pan, Lei He +1
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 mann…