151 citations · 423 across the 32 of their papers we have counts for
32 papers
Multi-speaker Text-to-speech Training with Speaker Anonymized Data
Wen-Chin Huang, Yi-Chiao Wu, Tomoki Toda
The trend of scaling up speech generation models poses a threat of biometric information leakage of the identities of the voices in the training data, raising privacy and security…
AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec
Yi-Chiao Wu, Israel D. Gebru, Dejan Marković +1
A good audio codec for live applications such as telecommunication is characterized by three key properties: (1) compression, i.e.\ the bitrate that is required to transmit the sig…
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…
A Cyclical Approach to Synthetic and Natural Speech Mismatch Refinement of Neural Post-filter for Low-cost Text-to-speech System
Yi-Chiao Wu, Patrick Lumban Tobing, Kazuki Yasuhara +3
Neural-based text-to-speech (TTS) systems achieve very high-fidelity speech generation because of the rapid neural network developments. However, the huge labeled corpus and high c…
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…
HASA-net: A non-intrusive hearing-aid speech assessment network
Hsin-Tien Chiang, Yi-Chiao Wu, Cheng Yu +4
Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. Recently, deep neural network (DNN) models hav…
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…
The NU Voice Conversion System for the Voice Conversion Challenge 2020: On the Effectiveness of Sequence-to-sequence Models and Autoregressive Neural Vocoders
Wen-Chin Huang, Patrick Lumban Tobing, Yi-Chiao Wu +2
In this paper, we present the voice conversion (VC) systems developed at Nagoya University (NU) for the Voice Conversion Challenge 2020 (VCC2020). We aim to determine the effective…
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),…
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
Wen-Chin Huang, Tomoki Hayashi, Yi-Chiao Wu +2
Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody. Nonetheless, without sufficient data, seq2seq VC models can su…
Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto +2
In this paper, we propose a quasi-periodic parallel WaveGAN (QPPWG) waveform generative model, which applies a quasi-periodic (QP) structure to a parallel WaveGAN (PWG) model using…
Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
Yi-Chiao Wu, Tomoki Hayashi, Patrick Lumban Tobing +2
In this paper, a pitch-adaptive waveform generative model named Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch controllability of vanilla WaveNet (WN) usin…
A Cyclical Post-filtering Approach to Mismatch Refinement of Neural Vocoder for Text-to-speech Systems
Yi-Chiao Wu, Patrick Lumban Tobing, Kazuki Yasuhara +3
Recently, the effectiveness of text-to-speech (TTS) systems combined with neural vocoders to generate high-fidelity speech has been shown. However, collecting the required training…
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation
Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto +2
In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG. PWG is a compact non-aut…
Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
Wen-Chin Huang, Tomoki Hayashi, Yi-Chiao Wu +2
We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models are a…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion
Wen-Chin Huang, Yi-Chiao Wu, Kazuhiro Kobayashi +6
We present a modification to the spectrum differential based direct waveform modification for voice conversion (DIFFVC) so that it can be directly applied as a waveform generation…
Non-Parallel Voice Conversion with Cyclic Variational Autoencoder
Patrick Lumban Tobing, Yi-Chiao Wu, Tomoki Hayashi +2
In this paper, we present a novel technique for a non-parallel voice conversion (VC) with the use of cyclic variational autoencoder (CycleVAE)-based spectral modeling. In a variati…