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Emotional voice conversion (EVC) aims to convert the emotion of speech fromone state to another while preserving the linguistic content and speakeridentity. In this paper, we study the disentanglement and recomposition ofemotional elements in speech through variational autoencoding Wassersteingenerative adversarial network (VAW-GAN). We propose a speaker-dependent EVCframework based on VAW-GAN, that includes two VAW-GAN pipelines, one forspectrum conversion, and another for prosody conversion. We train a spectralencoder that disentangles emotion and prosody (F0) information from spectralfeatures; we also train a prosodic encoder that disentangles emotion modulationof prosody (affective prosody) from linguistic prosody. At run-time, thedecoder of spectral VAW-GAN is conditioned on the output of prosodic VAW-GAN.The vocoder takes the converted spectral and prosodic features to generate thetarget emotional speech. Experiments validate the effectiveness of our proposedmethod in both objective and subjective evaluations.

VAW-GAN for Disentanglement and Recomposition of Emotional Elements in Speech