48 citations · 214 across the 19 of their papers we have counts for
8 papers · 1 filter
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel voice conversion (VC) is a technique for training voice converters without a parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and CycleGAN-…
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel voice conversion (VC) is a technique for learning mappings between source and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial n…
VoiceGrad: Non-Parallel Any-to-Many Voice Conversion with Annealed Langevin Dynamics
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +2
In this paper, we propose a non-parallel any-to-many voice conversion (VC) method termed VoiceGrad. Inspired by WaveGrad, a recently introduced novel waveform generation method, Vo…
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings among multiple domains without relying on parallel data. This is important but challenging owin…
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been…
WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation
Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko +1
WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference wi…
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion
Hirokazu Kameoka, Kou Tanaka, Damian Kwasny +2
This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pit…
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +1
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, wh…