Primary goal of an emotional voice conversion (EVC) system is to convert the
emotion of a given speech signal from one style to another style without
modifying the linguistic content of the signal. Most of the state-of-the-art
approaches convert emotions for seen speaker-emotion combinations only. In this
paper, we tackle the problem of converting the emotion of speakers whose only
neutral data are present during the time of training and testing (i.e., unseen
speaker-emotion combinations). To this end, we extend a recently proposed
StartGANv2-VC architecture by utilizing dual encoders for learning the speaker
and emotion style embeddings separately along with dual domain source
classifiers. For achieving the conversion to unseen speaker-emotion
combinations, we propose a Virtual Domain Pairing (VDP) training strategy,
which virtually incorporates the speaker-emotion pairs that are not present in
the real data without compromising the min-max game of a discriminator and
generator in adversarial training. We evaluate the proposed method using a
Hindi emotional database.

本文旨在解决情感语音转换领域的一个问题：将没有任何语音情感数据的说话人语音的情感样式从一种风格转换为另一种风格。通过使用双编码器和虚拟领域对偶策略，我们提出的方法在一定程度上解决了基于 GANS 的 EVC 的问题，而且在印地语情感语音数据库上进行了验证。