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The aim of the project is about the detection of the emotions elicited by the speaker while talking. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast, with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and low-pitched speech. Detection of human emotions through voice-pattern and speech-pattern analysis has many applications such as better assisting human-machine interactions. In particular, we are presenting a classification model of emotion elicited by speeches based on deep neural networks (CNNs), SVM, MLP Classification based on acoustic features such as Mel Frequency Cepstral Coefficient (MFCC). The model has been trained to classify eight different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). Our evaluation shows that the proposed approach yields accuracies of 86%, 84%, and 82% using CNN, MLP Classifier and SVM Classifiers, respectively, for 8 emotions using Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and Toronto Emotional Speech Set (TESS) Dataset.

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Comments

ROOPASREE P says:

Can u tell where u are taking input that part of code of is not given

Lakshminarayana N says:

Everything is fine sir, could u tell me how you deployed using django framework at last

Ritiy Biko says:

4:37 anyone has implemented and got the accuracy at 83%?

Amala Natu says:

Can you please tell me from where to download the data set?

murali badiginti says:

How can we store model using mlpclassifier

五二一 says:

Whoal professional

leona moniz says:

can I get the source code of this project and also how you deployed the project in django framework

satyasai myneni says:

Where is the source code?

Engineer says:

Can I get source code?

Ramya Vadlamudi says:

bro plz send the code

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