| Summary: | Feature selection plays a crucial role in establishing an effective speech emotion recognition system. To improve recognition accuracy, people always extract as many features as possible from speech signals. However, this may reduce efficiency. We propose a hybrid filter–wrapper feature selection based on a genetic algorithm specifically designed for high-dimensional (HGA) speech emotion recognition. The algorithm first utilizes Fisher Score and information gain to comprehensively rank acoustic features, and then these features are assigned probabilities for inclusion in subsequent operations according to their ranking. HGA improves population diversity and local search ability by modifying the initial population generation method of genetic algorithm (GA) and introducing adaptive crossover and a new mutation strategy. The proposed algorithm clearly reduces the number of selected features in four common English speech emotion datasets. It is confirmed by K-nearest neighbor and random forest classifiers that it is superior to state-of-the-art algorithms in accuracy, precision, recall, and F1-Score.
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