## 1 Introduction  
An enormous amount of voice data is being produced by the widespread adoption of voice technologies in our daily lives. Besides the use of speech biometrics for authentication [1], voice instructions to virtual assistants, and voice communications across multiple industries, social media is also causing an exponential increase in the amount of speech data available online [2]. This trend raises some serious issues, most notably those pertaining to security and privacy [3]. Voice recordings have the potential to disclose personal information such as age, gender, ethnic origin, and religious beliefs [4]. Modern voice biometrics have the capability to derive a variety of the aforementioned private and sensitive data from a speech signal. Given these concerns, and also as voice assistants and voice recognition technologies become more ubiquitous, protecting user identities within voice data is becoming increasingly vital. This has led to an increased interest in privacy preservation solutions for speech technology that, following the General Data Protection Regulation (GDPR) introduced by the European Union [5], aim to protect personal speech data. One popular approach to mitigate these issues is to develop privacy by design solutions, which perform the data processing on the edge devices and are close to where the data are generated. This solution avoids transferring or storing sensitive information on cloud infrastructures. However, the limited computational resources of edge nodes typically require the compression of AI models in order to reduce their memory and computation requirements [6; 7; 8]. In addition, this approach often requires the design of tailored solutions, which may not always be possible. Alternative approaches are based on encryption [9; 10]. Similarly, implementing jamming mechanisms on speech signals may obfuscate the user's identity [11; 12]. There are two main limitations of encryption: (1) the computational cost and (2) that the cloud entity processing the data, in any case, has access to the raw signals. Recently, techniques for voice conversion (VC), which enable the alteration of one's voice into another, have become a viable means of accomplishing privacy preservation via speech anonymization [13; 14; 15; 16]. Speaker anonymization is a user-centric voice privacy solution that aims to remove speaker information from a speech utterance while leaving the other acoustic attributes unaltered. In this way, it is possible to conceal a speaker's identity without compromising intelligibility and naturalness. Voice anonymization offers a transparent solution for the following speech processing strategies and does not have to be tailored or adapted to a specific privacy-preservation method. Figure 1 graphically shows these two strategies: privacy by design and voice anonymization. The VoicePrivacy Challenge [18] in 2020 and 2022 accelerated advancements in speaker anonymization techniques. Most approaches in the VoicePrivacy Challenge can be categorized into signal-processing-based voice transformation and voice conversion based on speaker embeddings [14; 19; 20; 21; 22; 23; 24; 25]. Signal anonymization based on processing does not require training data and directly alters speech characteristics like the time scale, the pitch, or the spectral envelope. As the range of physical shifts in voice signals is limited, attackers may be able to recover the original speech after a certain number of tries. Therefore, many modern anonymization systems are based on the process of separating the speaker-related representation from the raw speech signal using neural paradigms. These methods reduce speaker-specific characteristics by averaging or changing the speaker embeddings, which are often achieved using representations retrieved from a pre-trained automated speaker verification model. The final result is the disentanglement between the speech content and the speaker information [26; 27; 28].