Applying the proposed multi-task framework to real-world scenarios with noisy, unconstrained audio data presents several challenges. One significant limitation is the robustness of the model to variations in audio quality and background noise. The performance of the multi-task model may degrade when exposed to audio that deviates from the clean, controlled conditions typically used during training. This is particularly critical for tasks like ASR and SV, where background noise can obscure speech signals and affect the accuracy of transcriptions and speaker identification.
Another challenge is the alignment of feature spaces across different tasks when dealing with noisy data. The multi-teacher KD approach relies on the assumption that the feature representations from the teacher models are reliable. However, in noisy environments, the quality of these representations may be compromised, leading to suboptimal alignment and, consequently, poorer performance across all tasks.
Additionally, the model's ability to generalize to diverse audio conditions is crucial. The training data used for the teacher models may not encompass the full range of real-world audio scenarios, which can result in a lack of adaptability. To mitigate these issues, incorporating data augmentation techniques during training, such as adding synthetic noise or varying the audio conditions, could help improve the model's robustness. Furthermore, continuous learning strategies could be employed to adapt the model to new audio environments over time.