Volume IX, Issue IV

Title– A NOVEL SENTIMENTS ANALYSIS MODEL USING PERCEPTRON CLASSIFIER

Author– Alex M. Goh and Xiaoyu L. Yann

Abstract

Speech Emotion Recognition, abbreviated as SER, is used to recognize sentiments of humans and the related affective states from dialog. This is exploiting on the fact that vocal sound often reflects underlying emotion through tone and pitch. Emotion recognition is a rapidly growing research domain in recent years. Unlike humans, machines are genderless and lack the abilities to observe and display sentiments. But human-computer interaction can be improved by implementing automated emotion recognition, thereby reducing the need of human intervention. Here basic emotions like calm, happy, fearful, disgust etc. are analyzed from emotional speech signals. Machine learning techniques like Multilayer Perceptron Classifier (MLP Classifier) which is used to categorize the given data into respective groups which are non-linearly separated. Mel frequency cestrum coefficients (MFCC), chroma and Mel features are extracted from the speech signals and used to train the MLP classifier.

Index Term– Speech Emotion Recognition, Sentiment Analysis, Machine Learning, Artificial Intelligence, MLP, MFCC.

DOI- 10.30696/IJEEA.IX.IV.2021.01-10.

Reference to this paper should be made as follows:  Alex M. Goh and Xiaoyu L. Yann, (2021), “A Novel Sentiments Analysis Model Using Perceptron Classifier” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 4, pp. 01-10.