Microsoft

VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers

This paper introduces VALL-E 2, the latest advancement in neural codec language models that marks a milestone in zero-shot text-to-speech synthesis (TTS), achieving human parity for the first time.
GitHub

Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching

Small and fast: only 123M parameters. High-quality voice cloning: state-of-the-art performance in speaker similarity, intelligibility, and naturalness. Multi-lingual: support Chinese and English.
IEEE

Advancing Text-to-Speech Systems for Low-Resource Languages: Challenges, Innovations, and Future Directions

Abstract: Speech synthesis, the technology that converts text into spoken words, has advanced significantly for high-resource languages like English, Spanish, and Mandarin. However, many languages ...
GitHub

VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild

VoiceCraft is 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 in-the-wild data including ...
IEEE

SYNTHE-SEES: Face Based Text-to-Speech for Virtual Speaker

Abstract: Recent virtual voice generation researches have limitations in that they results in low-quality voice and generate inconsistent voice from the same speaker’s different facial images. To ...