151 citations · 423 across the 32 of their papers we have counts for
6 papers · 1 filter
Source-Filter HiFi-GAN: Fast and Pitch Controllable High-Fidelity Neural Vocoder
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
Our previous work, the unified source-filter GAN (uSFGAN) vocoder, introduced a novel architecture based on the source-filter theory into the parallel waveform generative adversari…
Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
This paper introduces a unified source-filter network with a harmonic-plus-noise source excitation generation mechanism. In our previous work, we proposed unified Source-Filter GAN…
Direct Noisy Speech Modeling for Noisy-to-Noisy Voice Conversion
Chao Xie, Yi-Chiao Wu, Patrick Lumban Tobing +2
Beyond the conventional voice conversion (VC) where the speaker information is converted without altering the linguistic content, the background sounds are informative and need to…
Noisy-to-Noisy Voice Conversion Framework with Denoising Model
Chao Xie, Yi-Chiao Wu, Patrick Lumban Tobing +2
In a conventional voice conversion (VC) framework, a VC model is often trained with a clean dataset consisting of speech data carefully recorded and selected by minimizing backgrou…
Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
We propose a unified approach to data-driven source-filter modeling using a single neural network for developing a neural vocoder capable of generating high-quality synthetic speec…
Baseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and Parallel WaveGAN
Patrick Lumban Tobing, Yi-Chiao Wu, Tomoki Toda
In this paper, we present a description of the baseline system of Voice Conversion Challenge (VCC) 2020 with a cyclic variational autoencoder (CycleVAE) and Parallel WaveGAN (PWG),…