In this paper, we develop two corpora for speech synthesis research. Thanks to
improvements in machine learning techniques, including deep learning, speech synthesis is …

Voice conversion (VC) has gained increasing popularity in many speech synthesis
applications. The idea is to change the voice identity from one speaker into another while …

Thanks to improvements in machine learning techniques, including deep learning, speech
synthesis is becoming a machine learning task. To accelerate speech synthesis research …

Thanks to developments in machine learning techniques, it has become possible to
synthesize high-quality singing voices of a single singer. An open multispeaker singing …

We present a method for improving the performance of crosslingual text-to-speech
synthesis. Previous works are able to model speaker individuality in speaker space via …

We propose novel deep speaker representation learning that considers perceptual similarity
among speakers for multi-speaker generative modeling. Following its success in accurate …

This paper proposes deep Gaussian process (DGP)-based frameworks for multi-speaker
speech synthesis and speaker representation learning. A DGP has a deep architecture of …

Decoding brain imaging data are gaining popularity, with applications in brain‐computer
interfaces and the study of neural representations. Decoding is typically subject‐specific and …

Multi-speaker speech synthesis is a technique for modeling multiple speakers' voices with a
single model. Although many approaches using deep neural networks (DNNs) have been …

This study investigates the information captured by speaker embeddings with relevance to
human speech perception. A Convolutional Neural Network was trained to perform one-shot …