# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 5 Experiment Results
### Fusion Feature Selection  
In the first stage, we first extracted the IS09_emotion, IS10_paraling, IS13_ComParE and embobase four global feature sets, and the LightGBM algorithm with the same configuration is used to conduct a five-fold cross-validation emotion classification experiment on each feature set. According to the average test accuracy of the five-fold classification experiments, the feature set with the best classification result is selected, and the results are shown in Figure 6.  
The IS13_ComParE feature set achieves the highest recognition accuracy when two different data are used as input, and the IS10_paraling feature set comes second, but the difference between its recognition ability and that of IS13_ComParE feature set is not much, within 1% in both cases. Considering that the feature dimensionality of IS13_ComParE is much larger than that of IS10_paraling, the relative performance improvement is not much, and IS10_paraling is finally selected as the fused global feature set here.  
In the second stage, we extracted the IS10_paraling feature set on the IEMOCAP dataset and used the following two statistical values as the method for feature importance selection while performing classification experiments on the LightGBM model: (1) \"split\": follow the split node when using a feature as a number of split attributes. (2) \"gain\": the total information gain that the feature brings to the split. A five-fold cross-validation was performed to accumulate the importance of the features obtained in the above two ways for each classification experiment. Here, 1500, 1200, and 800 features with the largest values were selected as the feature subset dimension for feature selection, and the five-times feature importance accumulation values were sorted from largest to smallest. The feature subsets were selected using feature subscripts of the first 1500, 1200, and 800 statistical values obtained, the three feature subsets obtained from feature selection were used again for classification experiments, and the experimental results are shown in Figure 7.  
The original IS10_paraling feature set of 1582-dimensional features is known to achieve an average recognition accuracy of 58.26% on IEMOCAP. It can be seen that after two methods of feature selection, the recognition accuracy of the original feature set is basically maintained using its 1500-dimensional and 1200-dimensional feature subsets. In addition, the 1200-dimensional feature subset obtained using the \"split\" filtering method achieves 59.3% recognition accuracy, which is 1% higher than the average accuracy of the original feature set. The 1200-dimensional feature subset obtained by the above method is used as the result of the feature selection experiment.  
Figure 6: Comparison of four global feature set classification results.  
We compare and select the global features, and determine the features with better relative performance and lower dimensions, which is beneficial to the subsequent feature fusion step and can improve the model recognition performance. This will be shown in detail later.