  Speech data on the Internet are proliferating exponentially because of the
emergence of social media, and the sharing of such personal data raises obvious
security and privacy concerns. One solution to mitigate these concerns involves
concealing speaker identities before sharing speech data, also referred to as
speaker anonymization. In our previous work, we have developed an automatic
speaker verification (ASV)-model-free anonymization framework to protect
speaker privacy while preserving speech intelligibility. Although the framework
ranked first place in VoicePrivacy 2022 challenge, the anonymization was
imperfect, since the speaker distinguishability of the anonymized speech was
deteriorated. To address this issue, in this paper, we directly model the
formant distribution and fundamental frequency (F0) to represent speaker
identity and anonymize the source speech by the uniformly scaling formant and
F0. By directly scaling the formant and F0, the speaker distinguishability
degradation of the anonymized speech caused by the introduction of other
speakers is prevented. The experimental results demonstrate that our proposed
framework can improve the speaker distinguishability and significantly
outperforms our previous framework in voice distinctiveness. Furthermore, our
proposed method also can trade off the privacy-utility by using different
scaling factors.
