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Vijayarajan V

Bio: Vijayarajan V is an academic researcher. The author has contributed to research in topics: Multilayer perceptron. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

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TL;DR: In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered and Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model.
Abstract: For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN.

1 citations


Cited by
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TL;DR: Wang et al. as mentioned in this paper used Support Vector Machine (SVM) and Recurrent Neural Network (RNN) to classify the PAD data and achieved 91.44% accuracy.

2 citations