## 6 Conclusions and Future Work  
Multi-task learning can learn robust and generalized feature representations from multiple tasks to better enable knowledge sharing among tasks. That is why it is widely used in various fields, and this is no exception in SER. However, previous studies have focused on applying MTL as a learning algorithm and have ignored the interpretability and generalized feature representations of the method. In this study, we break through the limitations of previous studies and propose an explainable multitask-based shared feature learning model using the gradient normalization adaptive loss balancing method. We establish the association between emotions and languages by jointly training language recognition and emotion recognition tasks to address the challenge that acoustic features are overly sensitive to monolinguals and make speech undergenerated in multilingual scenarios. Our model can learn a set of shared emotional features in the multilingual case and explain why multi-task learning is beneficial to improve the generalization ability of the model. Our proposed model can improve the interpretability of the multi-task learning model from the perspective of acoustic features and provide some reference significance for subsequent multilingual research. Experiments were conducted on three corpora: EMO-DB, CASIA, and SAVEE. The experimental results demonstrate that the present model is more effective in speech emotion recognition on the three corpora than other models. In summary: (1) The gradient normalization method could balance the weight of tasks efficiently by computing the gradient of each training step. Compared with adjusting the task weights based on experience, this saves the heavy workload of manually adjusting weights and improves the model performance. (2) The multi-task learning strategy can enable the MSFL to learn the generalized features of multiple languages to improve recognition performance and alleviate the confusion between emotions, such as anger and happiness in EMO-DB, and neutrality and sadness in SAVEE. (3) The top 10 multilingual shared features almost cover the top-ranked monolingual features, especially the _slopeV0-500_sma3 nz_aman_ that occurred in all three monolingual languages and ranked the first among the multilingual shared features. In addition, when analyzing the top 30 feature rankings, there are certain commonalities and differences among the three languages. The commonalities provide a theoretical basis  
Figure 8: The comparison of the top 30 features in EFs, CFs, SFs, and MSFs. for multi-language and multi-corpus data aggregation to alleviate the data sparsity. The differences promote the research prospect of multilingual speech emotion recognition. Future work will focus on the research of multilingual acoustic features and the multi-task SER model. For the multilingual acoustic features, we will increase the number of corpora for each language and mitigate the influence of extraneous factors other than language on speech emotion in the corpus, enhancing the generalizability of features across multiple corpora. On this basis, we will also attempt to fuse deep features to improve the performance of the model. Moreover, modalities such as text and facial expressions have also been developing rapidly in recent years, especially in facial expression recognition. EmoAffectNet [75] and Emotion-GCN [76] models have shown good performances, and multimodal features can also be combined with speech features in the future to explore commonalities and differences in multilingual emotional expressions from multiple modal cues [77]. For the multi-task model improvement, the first issue is related to the computational cost of the proposed model. Although the effect of MSFL is better than that of MTL_DNN, the running speed is twice slower than that of MTL_DNN, which also reminds us to take into account the model performance and model complexity together when improving the model in the future.