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- W2897863374 abstract "Speech1 Emotion Recognition SER uses the Berlin EMO-DB database, seven emotions. Traditional emotional features and their statistics are used in SER. Two improved Mel Frequency Cepstrum Coefficients MFCC features are added to this experiment, which extract MFCC parameters from the energy curve and the fundamental frequency curve, that are energy MFCC EEMFCC and fundamental frequency MFCC F0MFCC, using Support Vector Machines SVM as the recognition machine, we obtained the highest average recognition rate of 85.37% for the seven categories and 100% for sad." @default.
- W2897863374 created "2018-10-26" @default.
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- W2897863374 date "2018-10-22" @default.
- W2897863374 modified "2023-10-18" @default.
- W2897863374 title "Speech Emotion Recognition Based on Improved MFCC" @default.
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- W2897863374 doi "https://doi.org/10.1145/3207677.3278037" @default.
- W2897863374 hasPublicationYear "2018" @default.
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