Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Abstract

The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

Multi-Layer Hybrid Fuzzy Classification Based on SVM and Improved PSO for Speech Emotion Recognition

“…Considering the influence of classifier on SER system, we switch the base classifier of classification layer to KNN for comparison; that is, the classifier becomes multi-layer KNN. Meanwhile, we also compared the impact of using KNN [58] as a classifier and multi-layer KNN on system performance.…”

“…In Table 10, the success rate using the multi-layer KNN classifier increases by 2.94%, 6.09%, and 6.02% on three databases, respectively, compared with a single KNN classifier. Although the accuracy of multi-layer KNN has greatly improved compared that of KNN [58], the overall accuracy of multi-layer KNN is still lower than multi-layer SVM. Therefore, using a multi-layer can enhance the system's performance, and SVM performs better than KNN as the base classifier.…”