# A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender

arXiv: https://arxiv.org/abs/2101.08478 · HTML: https://mlsanity.com/paper/2101.08478/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2021.

## Built on by (most cited first, 9 of 9)

- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy](https://mlsanity.com/paper/2210.07002/) (2022)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [Improving speaker de-identification with functional data analysis of f0 trajectories](https://mlsanity.com/paper/2203.16738/) (2022)
- [Speaker Anonymization with Phonetic Intermediate Representations](https://mlsanity.com/paper/2207.04834/) (2022)
- [Are disentangled representations all you need to build speaker anonymization systems?](https://mlsanity.com/paper/2208.10497/) (2022)
- [Exploring VQ-VAE with Prosody Parameters for Speaker Anonymization](https://mlsanity.com/paper/2409.15882/) (2024)
- [Evaluating X-vector-based Speaker Anonymization under White-box Assessment](https://mlsanity.com/paper/2109.11946/) (2021)
- [Vocoder drift compensation by x-vector alignment in speaker anonymisation](https://mlsanity.com/paper/2307.08403/) (2023)

## Abstract (arXiv)

Speech pseudonymization aims at altering a speech signal to map the identifiable personal characteristics of a given speaker to another identity. In other words, it aims to hide the source speaker identity while preserving the intelligibility of the spoken content. This study takes place in the VoicePrivacy 2020 challenge framework, where the baseline system performs pseudonymization by modifying x-vector information to match a target speaker while keeping the fundamental frequency (F0) unchanged. We propose to alter other paralin-guistic features, here F0, and analyze the impact of this modification across gender. We found that the proposed F0 modification always improves pseudonymization We observed that both source and target speaker genders affect the performance gain when modifying the F0.