Characterization and Classification of Speech Emotion with Spectrograms

Palo, Hemanta Kumar; Sagar, Sangeet

doi:10.1109/iadcc.2018.8692126

Cited by 3 publications

(1 citation statement)

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“…Among many modalities of human affective states, the facial expression remains a significant mode of communicating an individual's state of mind. Facial expression accounts for 55% of the entire emotional information as compared to 38% by discourse, and 7% by language [1], [2]. Among these modalities, the recognition of emotions using facial expression (RFE) remains a complex domain of research due to the absence of standard best features adequately describing these states.…”

Section: Introductionmentioning

confidence: 99%

K-nearest neighbor based facial emotion recognition using effective features

Subudhiray

Palo

Das

2023

IJ-AI

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<span lang="EN-US">In this paper, an experiment has been carried out based on a simple k-nearest neighbor (kNN) classifier to investigate the capabilities of three extracted facial features for the better recognition of facial emotions. The feature extraction techniques used are histogram of oriented gradient (HOG), Gabor, and local binary pattern (LBP). A comparison has been made using performance indices such as average recognition accuracy, overall recognition accuracy, precision, recall, kappa coefficient, and computation time. Two databases, i.e., Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE) have been used here. Different training to testing data division ratios is explored to find out the best one from the performance point of view of the three extracted features, Gabor produced 94.8%, which is the best among all in terms of average accuracy though the computational time required is the highest. LBP showed 88.2% average accuracy with a computational time less than that of Gabor while HOG showed minimum average accuracy of 55.2% with the lowest computation time.</span>

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Section: Introductionmentioning

confidence: 99%