In this paper, a hybrid system is proposed for speech emotion recognition (SER). The algorithm in this paper adopts a two-stage design concept. In the first stage, we use the ensemble learning model random forest algorithm to obtain the importance of each feature. We use Emo-DB for experimental comparison and find that the combination of the logistic regression algorithm and the WBCS algorithm achieves best results. Cross-training method is used to ensure the features adapt to various situations. Under 100 training sets, the sentiment classification results are satisfactory. The proposed method is more accurate than state-of-the-art intelligent optimization dimensionality reduction algorithms.

本文提出了一种用于语音情感识别（SER）的混合系统。本文算法采用两阶段设计思想。在第一阶段，我们使用集成学习模型随机森林算法来获取每个功能的重要性。我们使用Emo-DB进行实验比较，发现逻辑回归算法和WBCS算法的组合可达到最佳效果。交叉训练方法用于确保特征适应各种情况。在100个训练集下，情绪分类结果令人满意。所提出的方法比最新的智能优化降维算法更准确。