To enhance the effectiveness of multi-teacher KD pre-training in capturing the relationships between different speech and audio processing tasks, several strategies can be implemented. First, the selection of teacher models could be diversified to include not only high-performing models for each task but also models that have been specifically designed to capture inter-task relationships. For example, a teacher model that has been trained on both ASR and speech translation could provide richer feature representations that are beneficial for both tasks.
Second, the loss functions used during KD could be refined to incorporate task-specific weights that reflect the importance of each task in the context of the overall learning objective. This would allow the model to prioritize learning from tasks that are more closely related or that provide complementary information, thereby improving the alignment of feature spaces.
Additionally, implementing a hierarchical KD approach could be beneficial. In this framework, the student model could first learn from a primary teacher model and then progressively incorporate knowledge from secondary teacher models. This staged approach would allow the student model to build a solid foundation before integrating more complex relationships between tasks.
Finally, leveraging self-supervised learning techniques during the KD process could enhance the model's ability to learn from unlabelled data. By utilizing large amounts of unlabelled audio data, the model could discover latent structures and relationships between tasks that may not be explicitly defined, leading to improved task alignment and overall performance.