activity
20182024
most citedLearning latent representations for style control and transfer in end-to-end speech synthesis

5 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2024

Drop the beat! Freestyler for Accompaniment Conditioned Rapping Voice Generation

Ziqian Ning, Shuai Wang, Yuepeng Jiang +5

Rap, a prominent genre of vocal performance, remains underexplored in vocal generation. General vocal synthesis depends on precise note and duration inputs, requiring users to have…

eess.AS2022★ 1 cited

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…

cs.SD2021★ 2 cited

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…

eess.AS2019★ 2 cited

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…

cs.CL2018★ 5 cited

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…