1 citations · 1 across the 4 of their papers we have counts for
4 papers
High Fidelity Text-to-Speech Via Discrete Tokens Using Token Transducer and Group Masked Language Model
Joun Yeop Lee, Myeonghun Jeong, Minchan Kim +3
We propose a novel two-stage text-to-speech (TTS) framework with two types of discrete tokens, i.e., semantic and acoustic tokens, for high-fidelity speech synthesis. It features t…
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…
Transduce and Speak: Neural Transducer for Text-to-Speech with Semantic Token Prediction
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +2
We introduce a text-to-speech(TTS) framework based on a neural transducer. We use discretized semantic tokens acquired from wav2vec2.0 embeddings, which makes it easy to adopt a ne…
Disentangled Speaker Representation Learning via Mutual Information Minimization
Sung Hwan Mun, Min Hyun Han, Minchan Kim +2
Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unrave…