Relying solely on speech recognition models as teacher models for capturing cross-modal correlation may have limitations, primarily related to the bias and limitations of the speech recognition algorithms. Speech recognition models are trained on specific datasets and may not generalize well to all types of audio content, especially in the presence of noise or variations in speech patterns.
To address these limitations, alternative approaches can be explored to capture cross-modal correlation effectively. One approach is to use unsupervised learning techniques to learn correlations between different modalities without relying on pre-trained models. This can involve methods like contrastive learning or self-supervised learning, where the model learns to align representations across modalities based on the inherent structure of the data.
Additionally, leveraging generative models like variational autoencoders (VAEs) or generative adversarial networks (GANs) can help in capturing complex correlations between modalities by generating synthetic data that represents the underlying relationships in the data. By exploring a combination of supervised, unsupervised, and generative approaches, the model can capture more robust and generalized cross-modal correlations for deepfake detection.