Machine Learning Approach for Emotion Recognition in Speech (Informatica, 2014)
This paper presents a machine learning approach to automatic recognition of human emotions fromspeech. The approach consists of three steps. First, numerical features are extracted from the sounddatabase by using audio feature extractor. Then, feature selection method is used to select the mostrelevant features. Finally, a machine learning model is trained to recognize seven universal emotions:anger, fear, sadness, happiness, boredom, disgust and neutral. A thorough ML experimental analysis isperformed for each step. The results showed that 300 (out of 1582) features, as ranked by the gain ratio,are sufficient for achieving 86% accuracy when evaluated with 10 fold cross-validation. SVM achievedthe highest accuracy when compared to KNN and Naive Bayes. We additionally compared the accuracyof the standard SVM (with default parameters) and the one enhanced by Auto-WEKA (optimizedalgorithm parameters) using the leave-one-speaker-out technique. The results showed that the SVMenhanced with Auto-WEKA achieved significantly better accuracy than the standard SVM, i.e., 73% and77% respectively. Finally, the results achieved with the 10 fold cross-validation are comparable andsimilar to the ones achieved by a human, i.e., 86% accuracy in both cases. Even more, low energyemotions (boredom, sadness and disgust) are better recognized by our machine learning approachcompared to the human.