8 citations · 18 across the 6 of their papers we have counts for
6 papers
Varianceflow: High-Quality and Controllable Text-to-Speech using Variance Information via Normalizing Flow
Yoonhyung Lee, Jinhyeok Yang, Kyomin Jung
There are two types of methods for non-autoregressive text-to-speech models to learn the one-to-many relationship between text and speech effectively. The first one is to use an ad…
NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis
Hyeong-Seok Choi, Jinhyeok Yang, Juheon Lee +1
Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, most of the voice synthesis…
Avocodo: Generative Adversarial Network for Artifact-free Vocoder
Taejun Bak, Junmo Lee, Hanbin Bae +3
Neural vocoders based on the generative adversarial neural network (GAN) have been widely used due to their fast inference speed and lightweight networks while generating high-qual…
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and Natural Non-Autoregressive Text-to-Speech
Jae-Sung Bae, Jinhyeok Yang, Tae-Jun Bak +1
This paper proposes a hierarchical and multi-scale variational autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to generate natural speech with diverse speakin…
GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis
Jinhyeok Yang, Jae-Sung Bae, Taejun Bak +2
Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the generation of reasonably good speech quality with a single model and made it possible to synthe…
VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network
Jinhyeok Yang, Junmo Lee, Youngik Kim +2
We present a novel high-fidelity real-time neural vocoder called VocGAN. A recently developed GAN-based vocoder, MelGAN, produces speech waveforms in real-time. However, it often p…