## 2 State of the Art
### Detection of Audio Fakes: A Technological Challenge
Finally, the CRIM system for ASVspoof2021 combines residual neural networks, time delay networks, and techniques such as HOSP, improving robustness in adverse conditions and providing effective solutions to counteract voice spoofing [42]. On the other hand, deep learning and biometric technologies have improved the accuracy of fake voice detection. ID R&D ([[https://www.idrnd.ai/id-rd-ranks-first-in-detecting-synthetic-speech-in-global-asvspoof-challenge/](https://www.idrnd.ai/id-rd-ranks-first-in-detecting-synthetic-speech-in-global-asvspoof-challenge/)]([https://www.idrnd.ai/id-rd-ranks-first-in-detecting-synthetic-speech-in-global-asvspoof-challenge/](https://www.idrnd.ai/id-rd-ranks-first-in-detecting-synthetic-speech-in-global-asvspoof-challenge/)), accessed on 23 October 2024) achieved the highest accuracy in ASVspoof 2019 with an error rate of 0.22%, using advanced synthetic voice detection techniques under the logical access (LA) condition. RawGAT-ST and RawNet focused on the analysis of speech patterns and spectro-temporal features using graph attention networks and raw audio analysis, respectively. RawGAT-ST achieved a 1.06% error rate at ASVspoof 2019 [43], while RawNet achieved the second best overall score [44]. Finally, other tools have focused on improving robustness and detection in challenging environments. The UR Channel-Robust Synthetic Speech Detection System, presented at ASVspoof 2021, focused on robustness to variability in the transmission channel, achieving an error rate of 5.46% in the logical access condition [45]. DKU-CMRI and Biometric Vox apply outlier detection models and advanced neural network architectures to address challenges at ASVspoof2021, achieving competitive error rates in logical and physical access detection tasks [46; 47]. In this context, the tool FastAudio [48] which was presented at ASVspoof2021, was selected for our project, due to its open source nature and its good results observed after different tests. Entities like ASVspoof and ADD play a crucial role in the innovation and development of technologies to detect synthetic voices and audio deepfakes, critical in the fight against disinformation and fake news. As we have seen, in recent years, advances in audio deepfake detection have been significant thanks to the use of advanced AI techniques. However, challenges continue to exist and new challenges arise that limit the capabilities of these models. One of the most complicated aspects of detecting audio deepfakes is the variety of types of manipulations. For example, detecting speech conversions (VCs) turns out to be more complex compared to text-to-speech synthesis (TTS). VC attacks usually present greater diversity, which makes their detection difficult [49]. On some occasions, these detection models also face adverse conditions such as noise in the audio, echo, or variability in the transmission channel that also hinder the detection process. Finally, another important limitation that we detected in the process of developing technology for audio deepfake detection is the scarcity of datasets. Most models are trained with a set of very similar and limited datasets, which does not reflect the diversity of situations found in audio deepfake attacks in the real world. Furthermore, finding fake audio datasets from specific people or in languages different from English (in our case Spanish) is even more complicated. Finally, training these models also requires great computing capacity and, in turn, economic resources, which is another major limitation for many projects [50].