Emotional Speech Classifier Systems: For Sensitive Assistance to support Disabled Individuals

Abstract

This paper provides the classification of emotionally annotated speech of mentally impaired people. The main problem encoun-tered in the classification task is the class-imbalance. This imbalance is due to the availability of large number of speech samples for the neutral speech compared to other emotional speech. Different sampling methodologies are explored at the back-end to handle this class-imbalance problem. Mel-frequency cepstral coefficients (MFCCs) features are considered at the front-end, deep neural networks (DNNs) and gradient boosted decision trees (GBDT) are investigated at the back-end as classifiers. The experimental results obtained from the EmotAsS dataset have shown higher classification accuracy and Unweighted Average Recall (UAR) scores over the baseline system.