While SRMHA and CFFN techniques offer significant benefits in reducing attention redundancy and parameter count, there are potential limitations and trade-offs to consider. One limitation of SRMHA is that it may introduce additional computational overhead due to the need for updating attention scores and managing residual connections. This could impact the overall efficiency of the model, especially in scenarios with limited computational resources. To address this, optimizing the implementation of SRMHA through parallel processing or efficient memory management could mitigate these challenges.
Similarly, CFFN may face trade-offs in terms of capturing complex spatial knowledge and maintaining model performance. Dividing the feed-forward networks into chunks could potentially limit the network's ability to learn intricate feature representations across the entire input sequence. To overcome this, exploring adaptive chunking strategies or incorporating dynamic chunk selection mechanisms based on input complexity could help mitigate these limitations and enhance the model's performance.
In future research, addressing these limitations could involve conducting in-depth analyses of the computational costs and performance trade-offs associated with SRMHA and CFFN. Additionally, exploring hybrid approaches that combine these techniques with other lightweighting methods could offer a more balanced solution for optimizing speech recognition models.