## 1 Introduction  
Speech emotion recognition (SER), which aims to recognize the emotion of speakers via extracting the acoustic features from the speech signal, started in the 1990s [1] and is a key research area in affective computing. Although modalities such as facial expressions, text, and physiological signals are known to have important and prominent results in affective computing [2, 3, 4], research reveals that speech modality, as the most convenient and natural medium of communication, has many advantages over the modalities described above [5]: compared with facial expressions, speech is a stronger temporal sequence, and it is easy to identify emotional changes from the entire sequence; compared with text, speech is more expressive in its intonation; compared with physiological signals such as those taken from an electroencephalogram (EEG), it is easy to collect speech data through the lightweight devices. In view of these advantages, SER has made breakthroughs in theoretical methods and key technologies after nearly three decades of development [6, 7, 8] and is widely used in intelligent vehicles [9], distance education [10], medical treatment [11], media retrieval systems [12] and other fields. However, emotion implies huge intra-class and inter-class differences, and objective factors such as gender [13], age [14], language [15], and speaker [16] reduce the performance of the existing methods. Since most studies in SER focus on a single language, multilingual SER has been addressed only in a few studies, causing us to focus on the effect of diverse languages on SER. [PERSON] et al. [17] found that the effect of SER in the same language or within the same language family is higher than that across languages or language families, which means that the model will be limited by the trained corpus due to the single language category and the small sample size of the corpus. This is also the case in real life. Taking SER in the teaching classroom as an example, language courses have lower emotion recognition performance than non-language courses due to the poor generalization of the model in multilingual scenarios. In other words, existing models mainly focus on feature learning within the corpus to improve the performance of SER, without considering the influence of different languages in multiple corpora, far from the SER in real complex situations. A natural question arises: how can we improve SER in multilingual scenarios? To answer this question, the connection between different languages and SER is investigated in this research, and the key challenges are summarized as follows: (1) What are the similarities in the feature representation of speech emotion expression in different languages? (2) How can we build a generalization model that can improve the performance of SER in multiple languages simultaneously? To address the above challenges, scholars have attempted to find breakthroughs from the perspective of features and models. For the first issue, researchers have explored fusing multiple features to find the features that are more conducive to multilingual emotional expression, and lead to an improvement. Currently, feature fusion has included the fusion between traditional handcrafted features [18; 19; 20; 21; 22; 23], the fusion between traditional handcrafted features and deep features [24; 25; 26; 27], and the fusion between deep features [28; 29]. In addition, feature selection is also an effective way to obtain optimal acoustic features. [PERSON] et al. [30] used a three-layer model consisting of acoustic features, semantic primitives, and emotion dimensions to map multilingual acoustic features to emotion dimensions by using a model inspired by human emotion perception ability, where acoustic features were selected using Fisher discriminant ratio and sequential forward selection methods were used to select features and develop a shared standard acoustic parameter set. For the second issue, the main point is how to control the influence of language as an objective factor on SER.