Voice Privacy Using Time-Scale and Pitch Modification

There is a growing demand toward digitization of various day-to-day work and hence, there is a surge in use of Intelligent Personal Assistants. The extensive use of these smart digital assistants asks for security and privacy preservation techniques because they use personally identifiable characteristics of the user. To that effect, various privacy preservation techniques for different types of voice assistants have been explored. Hence, for voice-based digital assistants, we need a privacy preservation technique. Thus, in this study, we explored the prosody modification methods to modify speaker-specific characteristics of the user, so that the modified utterances can then be made publicly available to use for training of different speech-based systems. This study presents three data augmentation techniques as voice anonymization methods to modify the speaker-dependent speech parameters (i.e., \(F_{0}\) $F_0$ ). The voice anonymization and speech intelligibility are measured objectively using the automatic speaker verification (ASV) and automatic speech recognition (ASR) experiments, respectively, on development and test set of Librispeech dataset. For speed perturbation-based anonymization, up to 53.7% relative increased % EER is observed for a perturbation factor, \(\alpha =0.8\) $\alpha =0.8$ for both male and female speakers. For the same case, the % WER was adequate (less than the baseline system), reflecting the use of speed perturbation method as anonymization algorithm in a voice privacy system. The similar performance is observed for pitch perturbation with perturbation factor, \(\lambda =-300\) $\lambda =-300$ . However, the tempo perturbation could not found to be useful for speaker anonymization during the experiments with % EER in the order of 5–10 \(\%\) $\%$ .