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- W4322495088 abstract "Speech Emotion Recognition, as described in this study, uses Neural Networks to classify the emotions expressed in each speech (SER). It’s centered upon concept where voice tone and pitch frequently reflect underlying emotion. Speech Emotion Recognition aids in the classification of elicited emotions. The MLP-Classifier is a tool for classifying emotions in a circumstance. As wave signal, allowing for flexible learning rate selection. RAVDESS (Ryerson Audio-Visual Dataset Emotional Speech and Song Database data) will be used. To extract the characteristics from particular audio input, Contrast, MFCC, Mel Spectrograph Frequency, & Chroma are some of factors that may be employed. To facilitate extraction of features from audio script, dataset will be labelled using decimal encoding. Utilizing input audio sample, precision was found to be 80.28%. Additional testing confirmed this result." @default.
- W4322495088 created "2023-02-28" @default.
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- W4322495088 date "2023-02-27" @default.
- W4322495088 modified "2023-09-26" @default.
- W4322495088 title "Deep Learning-Based Speech Emotion Recognition" @default.
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- W4322495088 doi "https://doi.org/10.4028/p-0892re" @default.
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