Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is
important to capture the diversity in human speech such as speaker identities, prosodies …

Large-scale automatic speech translation systems today lack key features that help machine-
mediated communication feel seamless when compared to human-to-human dialogue. In …

Neural vocoders based on the generative adversarial neural network (GAN) have been
widely used due to their fast inference speed and lightweight networks while generating …

Speech synthesis has made significant strides thanks to the transition from machine learning
to deep learning models. Contemporary text-to-speech (TTS) models possess the capability …

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a
variant of the standard version (L1), which is challenging as L2 is different from L1 in terms …

We introduce an open source high-quality Mandarin TTS dataset MSceneSpeech (Multiple
Scene Speech Dataset), which is intended to provide resources for expressive speech …

Large generative language models have been used to solve various language-related
tasks. We explore whether such models can suggest appropriate prosody for expressive …

Speech synthesis has significantly advanced from statistical methods to deep neural
network architectures, leading to various text-to-speech (TTS) models that closely mimic …

Reference-based Text-to-Speech (TTS) models can generate multiple, prosodically-different
renditions of the same target text. Such models jointly learn a latent acoustic space during …

Text-to-speech (TTS) systems that scale up the amount of training data have achieved
significant improvements in zero-shot speech synthesis. However, these systems have …