## 6 Conclusions and Future Work  
Despite the current interest in the adoption and development of technologies for news verification, there is still not enough research on best practices of professional culture and multidisciplinary structures for the deployment of such technologies in the newsroom [58]. In this paper, we have presented the RTVE-UGR Chair multidisciplinary endeavor for the fight against disinformation and how we have organized the collaboration between different profiles within computing and journalism to develop effective tools for news verification, highlighting the importance and value of this multidisciplinary approach. State of the art on verification journalism demographics is mainly based on profiles that combine hybrid skills; for example, data journalists. However, as identified in [59], journalism remains by far the primary discipline of these professionals, with a remarkable gap in confidence between journalistic and computer science skills. In contrast, we argue in favor of a real multidisciplinary approach with different specialists with complementary backgrounds in journalism and computer science adopting specific roles. This is in line with the recent findings [60] emphasizing that collaboration between developers and journalists have a positive effect on both, as developers must work within the framework of the journalistic logic, while journalists acquire new technical competences. The generated voices and trained models provide a solid foundation for the detection of audio deepfakes, which is a growing need in this era of generative AI wherein information can be easily manipulated. The integration of third-party tools into our platform has enhanced the functionality and effectiveness of our application, providing the media with greater security when verifying audio and being able to work with various detection models. During the development of the RTVE-UGR Chair, we learned valuable lessons regarding the importance of news verification, specifically audio verification, and how it affects the advancement of voice generation technologies in society. A significant challenge was the training of audio verification models in Spanish and for the persons of interest selected by the Chair, given the limited availability and specificity of resources in this \begin{table}
\begin{tabular}{l c} \hline \hline  & **Fake Audios** & **Real Audios** \\ \hline True Positives (TP) & 354 & — \\ False Negatives (FN) & 0 & — \\ False Positives (FP) & — & 4 \\ True Negatives (TN) & — & 252 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of Experiment 4 (general model for all speakers of interest). language. To overcome this, we adopted a multidisciplinary and collaborative approach, involving both academic experts and industry professionals. This approach not only advanced the technology, but also highlighted the importance of public-private collaboration in technological innovation projects. In addition, we learned that continuous education and outreach are key to keeping pace with rapid technological advances and ensuring a practical and ethical application of these developments. This is in line with the results of the comprehensive study of AI guidelines in 17 countries presented in [61], where it is stated that the collaborative synergy between journalists and technologist is also a way to ensure that AI complements and augments journalistic efforts rather than supplanting them. The future of the RTVE-UGR Chair focuses on the expansion and continuous improvement of our capabilities:  
* Developing more models: We will continue to develop models with more voices of persons of interest, expanding our dataset and improving the accuracy of our models. * Improved detection capabilities: We are working on introducing more voices generated using different fake voice creation methods to improve the detection of tampering and spoofing.