Cross Languages One-Versus-All Speech Emotion Classifier

Speech emotion recognition (SER) is a task that cannot be accomplished solely depending on linguistic models due to the presence of figures of speech. For a more accurate prediction of emotions, researchers adopted acoustic modelling. The complexity of SER can be attributed to a variety of acoustic features, the similarities among certain emotions, etc. In this paper, we proposed a framework named Cross Languages One-Versus-All Speech Emotion Classifier (CLOVASEC) that identifies speeches’ emotions for both Chinese and English. Acoustic features were preprocessed by Synthetic Minority Oversampling Technique (SMOTE) to diminish the impact of an imbalanced dataset then by Principal component analysis (PCA) to reduce the dimension. The features were fed into a classifier that was made up of eight sub-classifiers and each sub-classifier was tasked to differentiate one class from the other seven classes. The framework outperformed regular classifiers significantly on The Chinese Natural Audio-Visual Emotion Database (CHEAVD) and an English dataset from Deng.