## 4 Experimental Setup
### Speech Features Extraction  
Acoustic features convey emotional information independently from the linguistic content, and it is crucial to extract and select appropriate features due to the different correlations between varied features and emotions. Currently, feature representation can be divided into traditional handcrafted features and depth features. One of the traditional handcrafted features is low-level descriptors (LLDs) such as prosody features, voice quality features, spectral features, and TEO (Teager energy operator)-based features, which are also called local features extracted from each frame in an utterance. The other is the HSFs, which are also called global features and are calculated as statistics of all local features [71]. Depth features utilize the advantage where deep learning has the great ability of feature learning, to automatically learn deeper features from the original speech signal or spectrogram [72]. Although the application of depth features boosts the development of the end-to-end model and enables the improved performance of SER, it has also led to the disadvantages of poor interpretability, high memory usage, and increased computational complexity.  
Since one of our focuses is explaining the shared features of different languages, we use traditional handcrafted features with better interpretability as the features. To this end, we leverage the feature set of the Extended Geneva Minimalistic Acoustic Parameter Set (eGeMaps) as emotional acoustic features, which is extracted by the OpenSMILE toolkit and has better parameter interpretation and generalization capability than large-scale  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Corpus**} & \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Actors**} & \multirow{2}{*}{**Sampling Rate**} & \multicolumn{4}{c}{**Emotions**} \\ \cline{5-8}  & & & & & **Neu** & & **Ang** & **Hap** & **Sad** \\ \hline EMO-DB & German & 339 & 10(5M, 5F) & 16 KHz & 79 & 127 & 71 & 62 \\ CASIA & Chinese & 800 & 4(2M, 2F) & 22.05 KHz & 200 & 200 & 200 & 200 \\ SAVEE & English & 300 & 4M & 44.1 KHz & 120 & 60 & 60 & 60 \\ \hline \hline \end{tabular} Abbreviations: M: Male, F: Female; Neu: Neutral; Ang: Anger; Hap: Happiness; Sad: Sadness.  
\end{table}
Table 1: The detailed information of corpora used in our experiment.  
high-dimensional baseline feature sets. The eGeMaps contains 25 LLDs broadly sorted by three parameter groups: frequency-related parameters, energy-related parameters (i.e., amplitude-related parameters), and spectral parameters. To mitigate noise in the speech signal acquisition process on emotion recognition, all LLDs were smoothed over time with a symmetric moving average filter three frames long. The details of eGeMaps are given in Table 2. In addition, six temporal features (i.e., the rate of loudness peaks, the mean length and the standard deviation of continuously voiced regions, the mean length of and the standard deviation of unvoiced regions, the number of continuous voiced regions per second) and equivalent soundlevel are included. In total, 88 dimensions of features are used in our experiments.