The proposed multi-task framework can be extended to accommodate a broader range of speech and audio processing tasks by incorporating additional teacher models and adapting the architecture to support new input features and output requirements. For instance, to include speech translation, a dedicated teacher model trained on translation tasks could be integrated into the multi-teacher knowledge distillation (KD) framework. This model would provide the necessary supervision for aligning the feature space of the student encoder with the translation task, similar to how the existing models for ASR, AT, and SV are utilized.
Moreover, for tasks like voice activity detection (VAD), the framework could be enhanced by adding a VAD-specific teacher model that focuses on distinguishing between speech and non-speech segments. This would require the student model to learn from both the temporal and spectral characteristics of audio, which could be achieved by modifying the input features to include temporal context or by employing different layers of the encoder that are more sensitive to these characteristics.
To ensure effective training, the loss functions for these new tasks should be carefully designed to avoid interference with existing tasks. This could involve using auxiliary losses during the fine-tuning stage, similar to the approach taken with ASR, AT, and SV. Additionally, the model architecture may need to be adjusted to accommodate the unique requirements of each new task, ensuring that the shared parameters do not degrade performance on any individual task.