25 citations · 55 across the 6 of their papers we have counts for
6 papers
Intelli-Z: Toward Intelligible Zero-Shot TTS
Sunghee Jung, Won Jang, Jaesam Yoon +1
Although numerous recent studies have suggested new frameworks for zero-shot TTS using large-scale, real-world data, studies that focus on the intelligibility of zero-shot TTS are…
FastFit: Towards Real-Time Iterative Neural Vocoder by Replacing U-Net Encoder With Multiple STFTs
Won Jang, Dan Lim, Heayoung Park
This paper presents FastFit, a novel neural vocoder architecture that replaces the U-Net encoder with multiple short-time Fourier transforms (STFTs) to achieve faster generation ra…
UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation
Won Jang, Dan Lim, Jaesam Yoon +2
Most neural vocoders employ band-limited mel-spectrograms to generate waveforms. If full-band spectral features are used as the input, the vocoder can be provided with as much acou…
Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains
Won Jang, Dan Lim, Jaesam Yoon
We propose Universal MelGAN, a vocoder that synthesizes high-fidelity speech in multiple domains. To preserve sound quality when the MelGAN-based structure is trained with a datase…
Real-time Mask Detection on Google Edge TPU
Keondo Park, Wonyoung Jang, Woochul Lee +4
After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing masks in order to reduce risk of front-line workers. In addition, processing…
JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment
Dan Lim, Won Jang, Gyeonghwan O +3
We propose Jointly trained Duration Informed Transformer (JDI-T), a feed-forward Transformer with a duration predictor jointly trained without explicit alignments in order to gener…