50 citations · 82 across the 13 of their papers we have counts for
13 papers
A Two-Step Approach for Data-Efficient French Pronunciation Learning
Hoyeon Lee, Hyeeun Jang, Jong-Hwan Kim +1
Recent studies have addressed intricate phonological phenomena in French, relying on either extensive linguistic knowledge or a significant amount of sentence-level pronunciation d…
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model
Hoyeon Lee, Hyun-Wook Yoon, Jong-Hwan Kim +1
Phrase break prediction is a crucial task for improving the prosody naturalness of a text-to-speech (TTS) system. However, most proposed phrase break prediction models are monoling…
Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis
Yuma Shirahata, Ryuichi Yamamoto, Eunwoo Song +3
Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models se…
Language Model-Based Emotion Prediction Methods for Emotional Speech Synthesis Systems
Hyun-Wook Yoon, Ohsung Kwon, Hoyeon Lee +4
This paper proposes an effective emotional text-to-speech (TTS) system with a pre-trained language model (LM)-based emotion prediction method. Unlike conventional systems that requ…
TTS-by-TTS 2: Data-selective augmentation for neural speech synthesis using ranking support vector machine with variational autoencoder
Eunwoo Song, Ryuichi Yamamoto, Ohsung Kwon +6
Recent advances in synthetic speech quality have enabled us to train text-to-speech (TTS) systems by using synthetic corpora. However, merely increasing the amount of synthetic dat…
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation
Ryo Terashima, Ryuichi Yamamoto, Eunwoo Song +4
Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are availabl…
Improved parallel WaveGAN vocoder with perceptually weighted spectrogram loss
Eunwoo Song, Ryuichi Yamamoto, Min-Jae Hwang +3
This paper proposes a spectral-domain perceptual weighting technique for Parallel WaveGAN-based text-to-speech (TTS) systems. The recently proposed Parallel WaveGAN vocoder success…
TTS-by-TTS: TTS-driven Data Augmentation for Fast and High-Quality Speech Synthesis
Min-Jae Hwang, Ryuichi Yamamoto, Eunwoo Song +1
In this paper, we propose a text-to-speech (TTS)-driven data augmentation method for improving the quality of a non-autoregressive (AR) TTS system. Recently proposed non-AR models,…
Parallel waveform synthesis based on generative adversarial networks with voicing-aware conditional discriminators
Ryuichi Yamamoto, Eunwoo Song, Min-Jae Hwang +1
This paper proposes voicing-aware conditional discriminators for Parallel WaveGAN-based waveform synthesis systems. In this framework, we adopt a projection-based conditioning meth…
Neural text-to-speech with a modeling-by-generation excitation vocoder
Eunwoo Song, Min-Jae Hwang, Ryuichi Yamamoto +3
This paper proposes a modeling-by-generation (MbG) excitation vocoder for a neural text-to-speech (TTS) system. Recently proposed neural excitation vocoders can realize qualified w…
Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
Ryuichi Yamamoto, Eunwoo Song, Jae-Min Kim
We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network. In the proposed method, a non-autoreg…
Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systems
Ohsung Kwon, Eunwoo Song, Jae-Min Kim +1
In this paper, we propose a high-quality generative text-to-speech (TTS) system using an effective spectrum and excitation estimation method. Our previous research verified the eff…
Probability density distillation with generative adversarial networks for high-quality parallel waveform generation
Ryuichi Yamamoto, Eunwoo Song, Jae-Min Kim
This paper proposes an effective probability density distillation (PDD) algorithm for WaveNet-based parallel waveform generation (PWG) systems. Recently proposed teacher-student fr…