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Primary goal of an emotional voice conversion (EVC) system is to convert theemotion of a given speech signal from one style to another style withoutmodifying the linguistic content of the signal. Most of the state-of-the-artapproaches convert emotions for seen speaker-emotion combinations only. In thispaper, we tackle the problem of converting the emotion of speakers whose onlyneutral data are present during the time of training and testing (i.e., unseenspeaker-emotion combinations). To this end, we extend a recently proposedStartGANv2-VC architecture by utilizing dual encoders for learning the speakerand emotion style embeddings separately along with dual domain sourceclassifiers. For achieving the conversion to unseen speaker-emotioncombinations, we propose a Virtual Domain Pairing (VDP) training strategy,which virtually incorporates the speaker-emotion pairs that are not present inthe real data without compromising the min-max game of a discriminator andgenerator in adversarial training. We evaluate the proposed method using aHindi emotional database.

Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing