Machine Learning Classification Emotive Speech Expression
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Our work on emotive expression classification through speech analysis was presented at ISSC2021 by Zachary Dair. The approach combined affective prosody (Mel-frequency Cepstral Coefficient, Zero Crossing Rate, Chroma Energy Normalised) and semantic analysis (Bag-of-words model). A Convolutional neural network and Logistic regression model were combined to form an ensemble-based approach for the classification of emotive expressions from multi-modal data (audio, text). The approach builds upon existing work in emotion classification, sentiment analysis, and natural language processing techniques. Results demonstrate mixed accuracy across varied data sources, indicating the limitations and considerations of a generalised approach. There are direct benefits for Affective computing research as it enables (a) insight into the strengths and limitations of such models in correctly classifying emotion in relation to population differences (e.g. gender) and provides (b) a baseline for emotion classification in speech across the six canonical basic emotions (Anger, Fear, Disgust, Joy, Sadness, Surprise).