Takuhiro Kaneko

ATTS2S-VC: Sequence-to-sequence Voice Conversion with Attention and Context Preservation Mechanisms

2019

This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving sequence modeling such as speech …

Automatic Speech Pronunciation Correction with Dynamic Frequency Warping-Based Spectral Conversion

2018

This paper deals with the problem of pronunciation conversion (PC) task, a problem to reduce non-native accents in speech while preserving the original speaker identity. Although PC can be regarded as a special class of …

Nonparallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks

2020 · arXiv (Cornell University)

We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN. The main features of our method, called StarGAN-VC, are as follows: First, …

Maskcyclegan-VC: Learning Non-Parallel Voice Conversion with Filling in Frames

2021

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-VC2) are widely accepted as benchmark methods. However, owing to their insufficient ability to …