## 1 Introduction
Most of the existing studies have been conducted by training language recognition classifiers for model selection [31; 32] or improving the model to enhance the generalization [33; 34; 35; 36; 37; 38; 39]. Although these approaches show good results, there are still some limitations in these efforts. We can summarize as follows: (1) existing methods emphasize controlling the influence of the languages and ignore the intrinsic connection between languages. (2) Most studies either focus on the exploration between multilingual features or on the improvement of models, with little consideration given to studying models and features together. A more efficient approach alternative to address the above limitations is multi-task learning (MTL), which is inspired by the fact that humans can learn multiple tasks simultaneously and use the knowledge learned in one task to help the learning of the remaining tasks [40]. MTL can learn robust and generalized feature representations from multiple tasks to better enable knowledge sharing between tasks, with the core idea of reducing the risk of overfitting for each task by weighing the training information between tasks. From this, it can be assumed that if the machine learns both language recognition and emotion recognition, will the multilingual shared features learned during the MTL training contribute to the improvement of multilingual SER? The literature proves that this is a feasible method. [PERSON] [41] investigated multilingual SER across English and French using MTL trained with gender recognition and language recognition. Through comparative experiments, he confirmed that the MTL strategy can lead to further improvements under all conditions and is effective for multilingual SER. [PERSON] et al. [42] proposed a multi-task deep neural network with shared hidden layers and jointly trained several SER tasks from different corpora. This method achieved large-scale data aggregation and obtained feature transformation of all corpora through shared hidden layers. [PERSON] [43] combined 25 open-source datasets to create a relatively larger multilingual corpus, which shows good performance on a multilingual and multi-task learning SER system based on the multilingual pre-trained wav2 vec 2.0 model. In his experiments, several auxiliary tasks were performed, including gender prediction, language prediction, and three regression tasks related to acoustic features. [PERSON] et al. (2019) created a novel framework with the concept of residual adapters for multi-corpus SER in a deep transfer learning perspective, where the multi-task transfer experiment of the model trained a shared network for all datasets while only the adapter modules and final classification layers were specific to each dataset. Experiments showed that multi-task transfer experiment led to increased results for 21 of the 26 databases and achieved the best performance. From these studies, it is clear that MTL applied to SER is beneficial for aggregating data, sharing features, and establishing emotional representations. However, previous studies have only applied MTL to improve the generalization ability of models, and have not fully taken into account the interpretability of the model and its generated shared features. In other words, MTL should not only be a method to improve model generalization but is also an effective way to analyze and explain shared features. To this end, considering the variability of emotional expressions in different languages, we propose an explainable Multitask-based Shared Feature Learning (MSFL) model for multilingual SER, which can improve the SER performance of each language and effectively analyze multilingual shared features (MSFs). Based on the basic idea of MTL, the module can be divided into a task-sharing module and a task-specific module, where the task-sharing module is the key component of MSFL, as it undertakes the feature selection and transformation to uncover the generalized high-level discriminative representations.