5 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis
Taejun Bak, Jae-Sung Bae, Hanbin Bae +2
Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models. Prosodic speech can be generated by conditioning acou…
N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement
Gyeong-Hoon Lee, Tae-Woo Kim, Hanbin Bae +3
Recently, end-to-end Korean singing voice systems have been designed to generate realistic singing voices. However, these systems still suffer from a lack of robustness in terms of…
A Neural Text-to-Speech Model Utilizing Broadcast Data Mixed with Background Music
Hanbin Bae, Jae-Sung Bae, Young-Sun Joo +2
Recently, it has become easier to obtain speech data from various media such as the internet or YouTube, but directly utilizing them to train a neural text-to-speech (TTS) model is…
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…