Due to a constantly increasing amount of speech data that is stored in
different types of databases, voice privacy has become a major concern. To
respond to such concern, speech researchers have developed various methods for
speaker de-identification. The state-of-the-art solutions utilize deep learning
solutions which can be effective but might be unavailable or impractical to
apply for, for example, under-resourced languages. Formant modification is a
simpler, yet effective method for speaker de-identification which requires no
training data. Still, remaining intonational patterns in formant-anonymized
speech may contain speaker-dependent cues. This study introduces a novel
speaker de-identification method, which, in addition to simple formant shifts,
manipulates f0 trajectories based on functional data analysis. The proposed
speaker de-identification method will conceal plausibly identifying pitch
characteristics in a phonetically controllable manner and improve formant-based
speaker de-identification up to 25%.

本研究提出了一种新颖的说话者去识别方法，采用简单的共振峰偏移和基于函数数据分析的 f0 轨迹操作，可以在音素可控的方式下掩盖潜在的识别音调特征，提高了基于共振峰的语音匿名度最多达 25%。

使用 f0 轨迹的函数数据分析提高说话人去识别能力

Improving speaker de-identification with functional data analysis of f0 trajectories

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.

本研究基于 VoicePrivacy 2020 挑战，目的在于对语音进行偏执匿名化，使其不可辨认的同时保持可理解性。通过改变基频等次要语音特征进行偏执匿名化可以取得更好的效果，且源和目标说话者的性别也会影响效果。