Before revision:  Our approach leads to an increase in EER of up to 16.8 \\%DIF < in males and 8.4\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.abstract %DIF > in males and 11.1\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.
After revision:  Our approach leads to an increase in EER of up to 19.4 \\%DIF < in males and 8.4\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.abstract %DIF > in males and 11.1\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.