Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
Table 3: The averages of the global variance for 4 conversion examples: M2M, M2F, F2M, F2F, where F indicates female speaker and M indicates male.
['[EMPTY]', 'M2M', 'M2F', 'F2M', 'F2F']
This property can be observed by calculating the Global Variance (GV) over the spectrum. Higher global variance indicates sharpness of the converted speech. We evaluated the global variance for each of the frequency index for 4 conversion examples: male to male, male to female, female to male, and female to female. In each example 3 curves for 3 cases are plotted: (a) autoencoder of stage 1 alone without classifier-1, (b) complete stage 1 with classifier-1 and (c) proposed approach with stage 1 and stage 2. We can see in all cases the proposed approach (curves (c)) offered the best sharpness. offered the highest global variance. (c) (d), where the sharpness offered by the proposed approach can be observed.