activity
20182022
most citedNon-Parallel Voice Conversion with Cyclic Variational Autoencoder

13 citations · 20 across the 6 of their papers we have counts for

collaborators

13 papers

cs.SD20221 cited

Two-stage training method for Japanese electrolaryngeal speech enhancement based on sequence-to-sequence voice conversion

Ding Ma, Lester Phillip Violeta, Kazuhiro Kobayashi +1

Sequence-to-sequence (seq2seq) voice conversion (VC) models have greater potential in converting electrolaryngeal (EL) speech to normal speech (EL2SP) compared to conventional VC m…

cs.SD2021

A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion

Wen-Chin Huang, Kazuhiro Kobayashi, Yu-Huai Peng +4

We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, bu…

cs.SD20212 cited

Non-autoregressive sequence-to-sequence voice conversion

Tomoki Hayashi, Wen-Chin Huang, Kazuhiro Kobayashi +1

This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models. Inspired by the great success of NAR-S2S models such as…

eess.AS20212 cited

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…

eess.AS2020

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…

eess.AS2020

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…