50 citations · 57 across the 4 of their papers we have counts for
7 papers
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…
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
Min-Jae Hwang, Eunwoo Song, Ryuichi Yamamoto +2
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully…
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…
ExcitNet vocoder: A neural excitation model for parametric speech synthesis systems
Eunwoo Song, Kyungguen Byun, Hong-Goo Kang
This paper proposes a WaveNet-based neural excitation model (ExcitNet) for statistical parametric speech synthesis systems. Conventional WaveNet-based neural vocoding systems signi…