744 citations · 1.1k across the 5 of their papers we have counts for
5 papers
Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis
Sangyun Lee, Hyungjin Chung, Jaehyeon Kim +1
Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly…
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
Jaehyeon Kim, Jungil Kong, Juhee Son
Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-s…
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
Jungil Kong, Jaehyeon Kim, Jaekyoung Bae
Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. Although such methods improve the sampling efficiency and mem…
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
Jaehyeon Kim, Sungwon Kim, Jungil Kong +1
Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS…
FloWaveNet : A Generative Flow for Raw Audio
Sungwon Kim, Sang-gil Lee, Jongyoon Song +2
Most modern text-to-speech architectures use a WaveNet vocoder for synthesizing high-fidelity waveform audio, but there have been limitations, such as high inference time, in its p…