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 …

Recent advancements in generative speech models based on audio-text prompts have
enabled remarkable innovations like high-quality zero-shot text-to-speech. However …

Language models (LMs) have demonstrated the capability to handle a variety of generative
tasks. This paper presents the UniAudio system, which, unlike prior task-specific …

While recent large-scale text-to-speech (TTS) models have achieved significant progress,
they still fall short in speech quality, similarity, and prosody. Considering speech intricately …

Zero-shot text-to-speech aims at synthesizing voices with unseen speech prompts. Previous
large-scale multispeaker TTS models have successfully achieved this goal with an enrolled …

Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

Recent breakthroughs in zero-shot voice synthesis have enabled imitating a speaker's voice
using just a few seconds of recording while maintaining a high level of realism. Alongside its …

Recently, there has been a growing interest in the field of controllable Text-to-Speech (TTS).
While previous studies have relied on users providing specific style factor values based on …

We introduce VoiceCraft, a token infilling neural codec language model, that achieves state-
of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on …

The detection and localization of highly realistic deepfake audio-visual content are
challenging even for the most advanced state-of-the-art methods. While most of the research …