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- W4293054852 abstract "Emotions play a vital role in identifying a human's mental state. Emotions are the way to express one's outlook and mental state to others. In this paper, an analysis of speech [8] to extract the emotions has been done. The feature vector comprises components of the audio signal that portray specific features of the speaker, such as tone, pitch, and energy, which is significant to prepare the classifier model to precisely recognize a particular emotion. Extraction of Mel-frequency cepstral coefficients(MFCC) [3] has been done from the speech signals to create the dataset for the research work. The feature vector extracted in the training dataset is then sent to the classifier model. The test dataset will go through the extraction technique, following which the classifier would settle on a choice concerning the hidden feeling in the test audio. As a part of dataset preparation, four different datasets(i.e., SAVEE, RAVDESS, TESS, CREMA-D) have been used. Support vector machines [4], LSTM, random forests and CNN model have been used in this study for classifying emotions. 2D CNN outperformed all the other models by giving an accuracy of nearly 70% on the testing dataset." @default.
- W4293054852 created "2022-08-25" @default.
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- W4293054852 date "2022-06-24" @default.
- W4293054852 modified "2023-10-18" @default.
- W4293054852 title "Speech Emotion Recognition" @default.
- W4293054852 doi "https://doi.org/10.1109/conit55038.2022.9848265" @default.
- W4293054852 hasPublicationYear "2022" @default.
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