Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning

Abstract

Speech Emotion Classification (SEC) relies heavily on the quality of feature extraction and selection from the speech signal. Improvement on this to enhance the classification of emotion had attracted significant attention from researchers. Many primitives and algorithmic solutions for efficient SEC with minimum cost have been proposed; however, the accuracy and performance of these methods have not yet attained a satisfactory point. In this work, we proposed a novel deep transfer learning approach with distinctive emotional rich feature selection techniques for speech emotion classification. We adopt mel-spectrogram extracted from speech signal as the input to our deep convolutional neural network for efficient feature extraction. We froze 19 layers of our pretrained convolutional neural network from re-training to increase efficiency and minimize computational cost. One flattened layer and two dense layers were used. A ReLu activation function was used at the last layer of our feature extraction segment. To prevent misclassification and reduce feature dimensionality, we employed the Neighborhood Component Analysis (NCA) feature selection algorithm for picking out the most relevant features before the actual classification of emotion. Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers were utilized at the topmost layer of our model. Two popular datasets for speech emotion classification tasks were used, which are: Berling Emotional Speech Database (EMO-DB), and Toronto English Speech Set (TESS), and a combination of EMO-DB with TESS was used in our experiment. We obtained a state-of-the-art result with an accuracy rate of 94.3%, 100% specificity on EMO-DB, and 97.2%, 99.80% on TESS datasets, respectively. The performance of our proposed method outperformed some recent work in SEC after assessment on the three datasets.