1. 1-R-43
　Neutral-to-Emotional Voice Conversion with Latent Representations of F0 using Generative Adversarial Networks
Zhaojie Luo, Tetsuya Takiguchi, and Yasuo Ariki (Kobe University)
Canonical Correlation Analysis
Overview
Background
Problems
Goal
1. Applying the continuous wavelet transform (CWT) and cross wavelet transform (XWT) method to systematically capture the F0 features of different temporal scales.
2. Using the VAE-GAN to train the MCC and AS-CWT features.
1. The representation of fundamental frequency (F0) is too simple for emotion conversion.
2. The emotional voice data is insufficient.
keep linguistic information unchanged　
Hey
Hey
neutral
sad
happy
angry
Emotional voice conversion
Emotional robot
Framework
L = LGAN + LDl like + Lprior
Training model
Dataset
Samples:
Results x E h G D x’ y
input
real data
ouput
Table 1 F0-RMSE results for different emotions. N2A, N2S and N2H represent the datasets from neutral to angry, sad and happy voice, respectively.
MOS evaluation of emotional voice conversion
Source LG NN VAE GAN VA-GAN
N2A
N2S
N2H