In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for
the deep neural network (DNN) based vector-to-vector regression. The goal of this work is …

We propose a postfilter based on a generative adversarial network (GAN) to compensate for
the differences between natural speech and speech synthesized by statistical parametric …

We propose a training framework for sequence-to-sequence voice conversion (SVC). A well-
known problem regarding a conventional VC framework is that acoustic-feature sequences …

We propose a learning-based postfilter to reconstruct the high-fidelity spectral texture in
short-term Fourier transform (STFT) spectrograms. In speech-processing systems, such as …

An effective approach for voice conversion (VC) is to disentangle linguistic content from
other components in the speech signal. The effectiveness of variational autoencoder (VAE) …

The paper presents a mechanism to perform speaker adaptation in speech synthesis based
on deep neural networks (DNNs). The mechanism extracts speaker identification vectors …

An effective approach to non-parallel voice conversion (VC) is to utilize deep neural
networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure …

This paper proposes architectures that facilitate the extrapolation of emotional expressions
in deep neural network (DNN)-based text-to-speech (TTS). In this study, the meaning of …

Speech Enhancement is a challenging and important area of research due to the many
applications that depend on improved signal quality. It is a pre-processing step of speech …

This paper describes the use of a low-dimensional vector representation of sentence
acoustics to control the output of a feed-forward deep neural network text-to-speech system …