3 citations · 4 across the 6 of their papers we have counts for
6 papers
MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech
Taejun Bak, Youngsik Eom, SeungJae Choi +1
Text-to-speech (TTS) systems that scale up the amount of training data have achieved significant improvements in zero-shot speech synthesis. However, these systems have certain lim…
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…
Hierarchical Context-Aware Transformers for Non-Autoregressive Text to Speech
Jae-Sung Bae, Tae-Jun Bak, Young-Sun Joo +1
In this paper, we propose methods for improving the modeling performance of a Transformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although the text encoder and aud…
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…