Effect on speech emotion classification of a feature selection approach using a convolutional neural network.

ABSTRACT: Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Emotionally related features are always extracted from speech signals for emotional classification. Handcrafted features are mainly used for emotional identification from audio signals. However, these features are not sufficient to correctly identify the emotional state of the speaker. The advantages of a deep convolutional neural network (DCNN) are investigated in the proposed work. A pretrained framework is used to extract the features from speech emotion databases. In this work, we adopt the feature selection (FS) approach to find the discriminative and most important features for SER. Many algorithms are used for the emotion classification problem. We use the

SUBMITTER: Amjad A 

PROVIDER: S-EPMC8576551 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature