Diffusion models are generative models, which gradually add and remove noise to learn the
underlying distribution of training data for data generation. The components of diffusion …

While current emotional text-to-speech (TTS) systems can generate highly intelligible
emotional speech, achieving fine control over emotion rendering of the output speech still …

Large-scale speech generation models have achieved impressive performance in the zero-
shot voice clone tasks relying on large-scale datasets. However, exploring how to achieve …

Expressive Text-to-Speech (TTS) using reference speech has been studied extensively to
synthesize natural speech, but there are limitations to obtaining well-represented styles and …

Diffusion Transformers have recently demonstrated unprecedented generative capabilities
for various tasks. The encouraging results, however, come with the cost of slow inference …

Transformer-based diffusion models have achieved significant advancements across a
variety of generative tasks. However, producing high-quality outputs typically necessitates …

In recent years, speech diffusion models have advanced rapidly. Alongside the widely used
U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have …

Although speech synthesis systems have remarkably advanced with their expansion into
various applications, achieving robust voice style transfer while maintaining high-quality in …