The proposed approaches for incorporating representations from multiple self-supervised models can be extended by implementing a more sophisticated fusion mechanism that can effectively combine the features from different models. One way to achieve this is by introducing a hierarchical fusion strategy where the representations from each model are first processed independently and then fused at different levels of abstraction. For example, the lower-level features can be combined early in the architecture, while higher-level features can be integrated closer to the output layer. This hierarchical fusion can help capture complementary information from each model and enhance the overall representation quality.
Additionally, employing a dynamic fusion mechanism that adaptively weights the contributions of each model based on the input data can further improve the integration process. By introducing learnable parameters that control the fusion process, the model can effectively leverage the strengths of each self-supervised model based on the context of the input audio. This dynamic fusion approach can enhance the flexibility and adaptability of the architecture when incorporating multiple representations.
Furthermore, exploring ensemble techniques where the predictions from individual models are combined at the output stage can also be beneficial. By training multiple models with different self-supervised representations and aggregating their outputs using techniques like averaging or stacking, the overall performance can be boosted. This ensemble approach can help mitigate the limitations of individual models and improve the robustness of the ASR system.