  Emotional Voice Conversion (EVC) aims to convert the emotional style of a
source speech signal to a target style while preserving its content and speaker
identity information. Previous emotional conversion studies do not disentangle
emotional information from emotion-independent information that should be
preserved, thus transforming it all in a monolithic manner and generating audio
of low quality, with linguistic distortions. To address this distortion
problem, we propose a novel StarGAN framework along with a two-stage training
process that separates emotional features from those independent of emotion by
using an autoencoder with two encoders as the generator of the Generative
Adversarial Network (GAN). The proposed model achieves favourable results in
both the objective evaluation and the subjective evaluation in terms of
distortion, which reveals that the proposed model can effectively reduce
distortion. Furthermore, in data augmentation experiments for end-to-end speech
emotion recognition, the proposed StarGAN model achieves an increase of 2% in
Micro-F1 and 5% in Macro-F1 compared to the baseline StarGAN model, which
indicates that the proposed model is more valuable for data augmentation.
