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W4386716259 abstract "In everyday life, people are often influenced by emotions in our behaviour and language. When people use different emotions to articulate the same text, it can have a completely different effect. With the increasing demand for speech emotion recognition (SER), more machine learning and deep learning methods are being used to perform SER. matlab was used as the experimental tool in this study. The Berlin Database of Emotional Speech was used as the database. Feature extraction was performed by Mel Frequency Cepstrum Coefficients (MFCC) and based on these feature values, Support Vector Machines (SVM), K-Nearest Neighbors Algorithm (KNN), Semi-supervised graph-based classifier, ECOC classification model, Naive Bayes model and long short-term memory for predictive classification. The results surface that among the six classifiers, the best sentiment recognition method is LSTM, with 93.2% and 73.03% accuracies in the training and test groups respectively." @default.
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W4386716259 date "2023-06-14" @default.
W4386716259 modified "2023-10-18" @default.
W4386716259 title "Speech emotion recognition using multiple classification models based on MFCC feature values" @default.
W4386716259 doi "https://doi.org/10.54254/2755-2721/6/20230449" @default.
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