Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

One-shot voice conversion (VC), which performs conversion across arbitrary speakers with
only a single target-speaker utterance for reference, can be effectively achieved by speech …

In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …

Although recent advances in voice conversion have shown significant improvement, there
still remains a gap between the converted voice and target voice. A key factor that maintains …

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to
identify unseen classes using the attribute. In this paper, we put forth a new GZSL technique …

Any-to-any voice conversion problem aims to convert voices for source and target speakers,
which are out of the training data. Previous works wildly utilize the disentangle-based …

Several voice conversion (VC) methods using a simple autoencoder with a carefully
designed information bottleneck have recently been studied. In general, they extract content …

We present a novel way of conditioning a pretrained denoising diffusion speech model to
produce speech in the voice of a novel person unseen during training. The method requires …

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-
linguistic input data, such as tables and attribute–value pairs, but overlook that different …

Factorizing speech as disentangled speech representations is vital to achieve highly
controllable style transfer in voice conversion (VC). Conventional speech representation …