Search Results for author: Byoung Jin Choi

Found 6 papers, 2 papers with code

SNAC: Speaker-normalized affine coupling layer in flow-based architecture for zero-shot multi-speaker text-to-speech

no code implementations30 Nov 2022 Byoung Jin Choi, Myeonghun Jeong, Joun Yeop Lee, Nam Soo Kim

Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a speech sample with the voice characteristic of an unseen speaker.

Speech Synthesis

Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech

no code implementations12 Oct 2022 Byoung Jin Choi, Myeonghun Jeong, Minchan Kim, Sung Hwan Mun, Nam Soo Kim

Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios with a set of pre-defined speakers.

Diff-TTS: A Denoising Diffusion Model for Text-to-Speech

1 code implementation3 Apr 2021 Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon, Byoung Jin Choi, Nam Soo Kim

Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency.

Denoising Speech Synthesis

Expressive Text-to-Speech using Style Tag

no code implementations1 Apr 2021 Minchan Kim, Sung Jun Cheon, Byoung Jin Choi, Jong Jin Kim, Nam Soo Kim

In this work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that utilizes a style tag written in natural language.

Language Modelling TAG

WaveNODE: A Continuous Normalizing Flow for Speech Synthesis

1 code implementation8 Jun 2020 Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Sung Jun Cheon, Byoung Jin Choi, Nam Soo Kim

In recent years, various flow-based generative models have been proposed to generate high-fidelity waveforms in real-time.

Speech Synthesis

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