5 papers
Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech
Semin Kim, Seungjun Chung, Taehong Moon +8
Recent advances in text-to-speech (TTS) models show impressive speech naturalness and quality, yet the role of large-scale open data in driving this progress remains underexplored.…
Semantic-Aware Reconstruction Error for Detecting AI-Generated Images
Ju Yeon Kang, Jaehong Park, Semin Kim +2
Recently, AI-generated image detection has gained increasing attention, as the rapid advancement of image generation technologies has raised serious concerns about their potential…
FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
Ju Yeon Kang, Ji Won Yoon, Semin Kim +2
Recently, fake audio detection has gained significant attention, as advancements in speech synthesis and voice conversion have increased the vulnerability of automatic speaker veri…
MakeSinger: A Semi-Supervised Training Method for Data-Efficient Singing Voice Synthesis via Classifier-free Diffusion Guidance
Semin Kim, Myeonghun Jeong, Hyeonseung Lee +3
In this paper, we propose MakeSinger, a semi-supervised training method for singing voice synthesis (SVS) via classifier-free diffusion guidance. The challenge in SVS lies in the c…
Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +3
We propose a novel text-to-speech (TTS) framework centered around a neural transducer. Our approach divides the whole TTS pipeline into semantic-level sequence-to-sequence (seq2seq…