Design Choices for X-vector Based Speaker Anonymization
Table 1: WER (%) obtained by ASReval on the dev and test sets.
['[BOLD] Distance', '[BOLD] Proximity', '[BOLD] Gender- selection', '[BOLD] Dev WER (%)', '[BOLD] Test WER (%)']
All the experiments are performed using the publicly available recipe of the VoicePrivacy Challenge. To qualitatively analyze the effect of anonymization over the source speakers’ x-vectors, we also compute the average PLDA distance between original and anonymized x-vectors over all trial utterances in the test set. Although random target selection produces comparable privacy protection and utility to dense, it limits the flexibility to select different regions in x-vector space. Compared to the sparse selection strategy, the dense strategy provides slightly better privacy protection in the Semi-Ignorant case, as well as higher utility Consequently we select the dense strategy in our third experiment. The distance is fixed to PLDA and proximity to dense. When we look at male trials in Fig. We also observe in Fig. Hence we can conclude that random gender selection is the best choice.