2 citations · 2 across the 1 of their papers we have counts for
2 papers
eess.AS2024★ 2 cited
Universal Score-based Speech Enhancement with High Content Preservation
Robin Scheibler, Yusuke Fujita, Yuma Shirahata +1
We propose UNIVERSE++, a universal speech enhancement method based on score-based diffusion and adversarial training. Specifically, we improve the existing UNIVERSE model that deco…
eess.AS2023
PromptTTS++: Controlling Speaker Identity in Prompt-Based Text-to-Speech Using Natural Language Descriptions
Reo Shimizu, Ryuichi Yamamoto, Masaya Kawamura +4
We propose PromptTTS++, a prompt-based text-to-speech (TTS) synthesis system that allows control over speaker identity using natural language descriptions. To control speaker ident…