Converting Anyone’s Emotion: Towards Speaker-Independent Emotional Voice Conversion
Table 1: A comparison of the MCD [dB], LSD [dB], PCC and RMSE [Hz] results for seen and unseen speakers converted by CycleGAN-based emotional VC framework [zhou2020transforming].
['[BOLD] Speaker', '[BOLD] MCD', '[BOLD] LSD', '[BOLD] PCC', '[BOLD] RMSE']
To validate the idea of speaker-independent emotion elements across speakers [schuller2005speaker, kotti2012speaker], we conduct a preliminary study using CycleGAN-based emotional voice conversion framework [zhou2020transforming], which is designed for speaker-dependent EVC. In this study, we train a network with two conversion pipelines for the mapping of spectrum and prosody (CWT-based F0 features) respectively. We train the network on one specific speaker and test it for both the intended (seen) and unseen speaker. [huang2019investigation, qian2019zero, Qian_2020, sisman2019group]; and that of prosody conversion in terms of Pearson correlation coefficient (PCC) and root mean square error (RMSE) of F0 contours [sisman2019group]. Zero effort represents the cases where we directly compare the speech of source and target emotions without any conversion. We observe that the speaker-dependent CycleGAN system performs for the unseen speaker pretty well, which is encouraging. Encouraged by this observation, we propose a speaker-independent emotional voice conversion framework that converts anyone’s emotion.