Automatic Mean Opinion Score (MOS) prediction is employed to evaluate the
quality of synthetic speech. This study extends the application of predicted
MOS to the task of Fake Audio Detection (FAD), as we expect that MOS can be
used to assess how close synthesized speech is to the natural human voice. We
propose MOS-FAD, where MOS can be leveraged at two key points in FAD: training
data selection and model fusion. In training data selection, we demonstrate
that MOS enables effective filtering of samples from unbalanced datasets. In
the model fusion, our results demonstrate that incorporating MOS as a gating
mechanism in FAD model fusion enhances overall performance.
Expert Commentary: The Role of Predicted MOS in Fake Audio Detection
Automatic Mean Opinion Score (MOS) prediction has been widely used in the field of multimedia information systems to evaluate the quality of synthetic speech. However, this study takes a step further by extending the application of predicted MOS to the task of Fake Audio Detection (FAD). By leveraging MOS, we can now assess how close synthesized speech is to the natural human voice, which is crucial in determining the authenticity of audio content.
Multi-disciplinary Nature of the Concepts
The concepts discussed in this article highlight the multi-disciplinary nature of multimedia information systems. It brings together expertise from various domains such as speech synthesis, audio analysis, and machine learning. By combining these fields, researchers and practitioners can develop more robust systems for detecting fake audio.
Animations, Artificial Reality, Augmented Reality, and Virtual Realities are closely related to multimedia information systems. While this article specifically focuses on audio content, these technologies often involve the integration of audiovisual elements to create immersive experiences. The ability to accurately detect fake audio is essential in maintaining the integrity of such systems and preventing misinformation or malicious manipulation.
Training Data Selection
The use of MOS in training data selection is a significant advancement in the field of Fake Audio Detection. Unbalanced datasets can pose challenges in accurately training models, as the imbalance may lead to biased results. By leveraging MOS, researchers can effectively filter samples and ensure that the training dataset represents a diverse range of voice qualities. This ultimately improves the performance and generalizability of the FAD models.
Model Fusion
Incorporating MOS as a gating mechanism in FAD model fusion is another key contribution highlighted in this article. Model fusion involves combining multiple models or techniques to enhance overall performance. By using MOS as a gating mechanism, the FAD system can prioritize the models with higher MOS values, indicating a closer resemblance to the natural human voice. This approach not only improves the accuracy of fake audio detection but also provides insights into the quality of synthesized speech.
Future Directions
As the field of multimedia information systems continues to evolve, the integration of MOS in various applications holds promise for future advancements. Predicted MOS can be further employed in areas such as video analysis, virtual reality experiences, and even deepfake detection. By considering MOS as a metric for assessing quality and authenticity, researchers can develop more comprehensive and reliable systems.
In conclusion, this article showcases the potential of predicted MOS in Fake Audio Detection. The multi-disciplinary nature of the concepts discussed highlights the interconnectedness of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By incorporating MOS in training data selection and model fusion, researchers pave the way for more accurate and robust systems in the detection of fake audio.