## 6 Conclusions  
Voice anonymization, as it is driven by voice conversion techniques, plays a pivotal role in addressing the complex intersection of privacy, security, and ethical concerns associated with the ever-expanding realm of voice data. As the adoption of voice technology continues to grow, the demand for effective voice anonymization solutions will only intensify. The present work has provided an overview of the importance of voice anonymization and the primary methods investigated in possible applications. One important approach is voice conversion, which makes use of self-representation learning to provide a strong framework for addressing the difficulties associated with speaker anonymization. Following a thorough examination of various methods, a promising strategy based on feature disentanglement was created and discussed. The goal of the proposed strategy is to conceal the speaker information contained in speech signals while maintaining content and comprehensibility using a specifically trained speech encoder. The experimental results validate the approach, although a more comprehensive analysis on the quality of the generated signals is suggested. Similarly, a specific study on residual speaker characteristics embedded in the learned representation would permit a fine-grained control of the converted speech when compared to the very recent VoicePAT framework [81] selected for the incoming VoicePrivacy Challenge 2024.  
A more complete anonymization speech pipeline may also include a redaction or obfuscation of specific content, such as, for example, spoken named entities [82; 83]. However, while the discussed techniques are essentially language-independent, audio de-identification is a more recent field of study that depends on the spoken language being used and requires the integration of multilingual ASR models to cope with general speakers.  
The implementation of the disentanglement procedure will be made available in a public repository.  
Conceptualization and Methodology, M.M. and A.B.; software, validation, formal investigation, S.F. and M.M.; writing, S.F., M.M., A.B.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the EU H2020 project MARVEL (project number: 957337).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **EER \(\uparrow\)** & **WER \(\downarrow\)** \\ \hline Original & 1.2 & 5.29 \\ \hline McAdams [45] & 5.6 & 11.74 \\ Pitch/Formants shift [36] & 35.2 & 10.27 \\ \hline AGAIN-VC [78] & 44.4 & 44.37 \\ PPG-VC [19] & 46.1 & 12.93 \\ \hline WavLM HS-768 [17] & 33.5 & 7.50 \\ S3 PRL [66] using WavLM & 37.9 & 6.24 \\ HS-768 & & \\ \hline +Disentanglement & **49.4** & 6.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results from using various Voice Conversion techniques on the Librispeech _test-clan_ set. The considered techniques were two signal-based approaches (McAdams and the alteration of pitch and formants); two state-of-the-art voice conversion techniques (AGAIN-VC and PPG-VC); models based on Self-Supervised Learning (SSL) representations that consider the last internal layer (Hidden Size 1024 or 768) or the CTC head (size 32 or 31) as content encoders; and finally, a WavLM model using S3 PRL-VC [66] were trained, with the disentanglement procedure further boosting the performance. “Original” refers to the original audio signals prior to conversion.