19 citations · 35 across the 7 of their papers we have counts for
7 papers
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Huaizhen Tang, Xulong Zhang, Jianzong Wang +4
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a conditional autoencoder based method, achieved excelle…
LVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation
Zhen Zeng, Jianzong Wang, Ning Cheng +1
In this paper, we propose a novel conditional convolution network, named location-variable convolution, to model the dependencies of the waveform sequence. Different from the use o…
GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis
Aolan Sun, Jianzong Wang, Ning Cheng +4
This paper introduces a graphical representation approach of prosody boundary (GraphPB) in the task of Chinese speech synthesis, intending to parse the semantic and syntactic relat…
MelGlow: Efficient Waveform Generative Network Based on Location-Variable Convolution
Zhen Zeng, Jianzong Wang, Ning Cheng +1
Recent neural vocoders usually use a WaveNet-like network to capture the long-term dependencies of the waveform, but a large number of parameters are required to obtain good modeli…
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit
Zhen Zeng, Jianzong Wang, Ning Cheng +1
Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of pr…
AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignment
Zhen Zeng, Jianzong Wang, Ning Cheng +2
Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel. AlignTTS is based on a Feed-Forward Transformer which generates mel-…
GraphTTS: graph-to-sequence modelling in neural text-to-speech
Aolan Sun, Jianzong Wang, Ning Cheng +3
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs co…