###### Abstract  
This paper presents the multidisciplinary work carried out in the RIVE-UGR Chair within the IVERES project, whose main objective is the development of a tool for journalists to verify the veracity of the audios that reach the newsrooms. In the current context, voice synthesis has both beneficial and detrimental applications, with audio deepfakes being a significant concern in the world of journalism due to their ability to mislead and misinform. This is a multifaceted problem that can only be tackled adopting a multidisciplinary perspective. In this article, we describe the approach we adopted within the RIVE-UGR Chair to successfully address the challenges derived from audio deepfakes involving a team with different backgrounds and a specific methodology of iterative co-creation. As a result, we present several outcomes including the compilation and generation of audio datasets, the development and deployment of several audio fake detection models, and the development of a web audio verification tool addressed to journalists. As a conclusion, we highlight the importance of this systematic collaborative work in the fight against misinformation and the future potential of audio verification technologies in various applications. audio deepfake; speech synthesis; voice conversion; news verification; misinformation +
Footnote †: journal: Applied  
Traditionally, to produce a good clone, a large number of hours of audio and considerable computing power were needed. However, there are various techniques that allow for sufficiently natural cloning with fewer resources, e.g., by reusing voices from third parties and adapting them to the characteristics of the target voice [13]. This makes it possible to obtain quite natural recordings with relative ease, especially from people for whom there is audiovisual content accessible on the Internet, such as celebrities or politicians. In the communication sector, the rise of new tools to synthesize voices with artificial intelligence (AI), many of them free and easily accessible [14], has aggravated the problem of false audios or \"audio deepfakes\" [15], posing a significant threat to the integrity of information [16]. For example, in journalism, the dissemination of manipulated audio can undermine public trust in the media, spreading misinformation and affecting public opinion on critical issues [17; 18]. To give some examples, in 2019, a scammer created a deepfake voice posing as a German executive to send a transfer of 220,000 euros to a Hungarian provider ([[https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402](https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402)]([https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402](https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402)), accessed on 23 October 2024). More recently, in January 2024, a call with [PERSON]'s fake voice asked citizens not to vote in New Hampshire ([[https://www.nbcnews.com/video/listen-fake-biden-robocall-tells-new-hampshire-not-to-vote-in-primary-202609733664](https://www.nbcnews.com/video/listen-fake-biden-robocall-tells-new-hampshire-not-to-vote-in-primary-202609733664)]([https://www.nbcnews.com/video/listen-fake-biden-robocall-tells-new-hampshire-not-to-vote-in-primary-202609733664](https://www.nbcnews.com/video/listen-fake-biden-robocall-tells-new-hampshire-not-to-vote-in-primary-202609733664)), accessed on 23 October 2024). The ability to distinguish between authentic and manipulated content is therefore more important than ever in the fields of security and communication; thus, audio verification becomes especially relevant [19]. In journalism, ensuring the authenticity of an audio is essential to maintain the credibility and trust of the public [20].