EGAN: A Neural Excitation Generation Model Based on Generative Adversarial Networks with Harmonics and Noise Input

Author:

Lin Yen-Ting1,Chiang Chen-Yu1

Affiliation:

1. National Taipei University,Dept. of Communication Engineering,New Taipei City,Taiwan

Publisher

IEEE

Reference30 articles.

1. Tacotron: Towards End-to-End Speech Synthesis;wang,2017

2. Quasi-Periodic Parallel WaveGAN: A Non-Autoregressive Raw Waveform Generative Model With Pitch-Dependent Dilated Convolution Neural Network

3. Fastspeech 2: Fast and high-quality end-to-end text to speech;ren,2020

4. FastSpeech: Fast, Robust and Controllable Text to Speech;ren,2019

5. Method for the subjective assessment of intermediate quality level of audio systems;series;Tech Rep Recommendation ITU-R BS 1534-3 ITU-R,2015

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