###### Abstract
In the legal field, manipulated audio can have significant implications in trials and legal procedures, and can lead to decisions based on falsified evidence. In the security sector, unverified audio can be used for scams, phishing, and biometric spoofing [21]. Several studies indicate that human beings still have great confidence in our ability to distinguish fake from real audio, which has proven misleading, since there is scientific evidence that in many cases even professionals are not able to do so [21; 22; 23]. Therefore, it is necessary to complement our criteria with detection tools that help us determine whether an audio is real or not. To address these challenges, in recent years the scientific community has been researching and developing various audio verification techniques, datasets, and tools [21; 24]. These include spectral analysis, which examines the unique characteristics of sound to identify irregularities, and voice biometrics, which uses the unique characteristics of a person's voice to verify their identity. Additionally, artificial intelligence and machine learning play a very important role in detecting audio \"deepfakes\" that may not be identified by humans [25]. However, despite their importance, there is limited research dedicated specifically to audio deepfakes compared to the attention received by video or image deepfakes [25]. This highlights the need for greater attention and resources in this area to develop more effective detection methods. Furthermore, in a recent report on disinformation [26], the OECD warns that the scale, speed of generation, and improvement in the quality of deepfakes require the adoption of effective measures that include the development of infrastructure and technological tools in public administrations that allow for the integrity of the information to be safeguarded and verified. As a coordinated multidisciplinary effort to face these challenges, the IVERES project (\"Identification, Verification and Answer. The democratic state faces the challenge of self-interested disinformation\") was proposed ([[https://iveres.es/](https://iveres.es/)]([https://iveres.es/](https://iveres.es/)), accessed on 23 October 2024), financed by the Spanish Ministry of Science and Innovation within the R&D&I program oriented to the challenges of society. IEVERES has recently been awarded the National Prize for Computer Engineering. The RTVE-UGR Chair has collaborated in this project, with the purpose of addressing the challenges related to deep audio fakes. The RTVE-UGR Chair in deep speech synthesis and conversational AI and its applications in news verification ([[https://catedrartve.ugr.es/](https://catedrartve.ugr.es/)]([https://catedrartve.ugr.es/](https://catedrartve.ugr.es/)), accessed on 23 October 2024) is a collaboration between the Spanish Radio and Television Corporation (RTVE) and the University of Granada (UGR), with the collaboration of Monoceros Labs. The chair was established in 2022 to promote research, technological development, innovation, and training in the field of speech and conversational technologies in the media. Among its objectives, those specifically related to audio fakes are to:  
* Develop and apply advanced methodologies to verify the authenticity of audio recordings, identifying manipulations, deepfakes, and any type of fake audio. * Test and evaluate existing fake audio detection models to determine their effectiveness. * Generation of audio datasets using artificial intelligence techniques to train the selected models with voices of interest. * Train and optimize models to improve their ability to detect fake audio. * Create useful and accessible tools for journalists that facilitate the quick and effective verification of audios. * Train journalists in the use of these tools.