38 citations · 69 across the 10 of their papers we have counts for
22 papers
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…
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Yi-Chiao Wu, Cheng-Hung Hu, Hung-Shin Lee +5
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available. Although a speaker-dependent (SD) vocoder usually outperforms a speaker…
The AS-NU System for the M2VoC Challenge
Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang +6
This paper describes the AS-NU systems for two tracks in MultiSpeaker Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a small number of 100 target utt…
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…
crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder
Kazuhiro Kobayashi, Wen-Chin Huang, Yi-Chiao Wu +3
In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank. Although we have released an open-source VC software based…
Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations
Wen-Chin Huang, Yi-Chiao Wu, Tomoki Hayashi +1
We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those unseen duri…