Speech emotion classification using attention based network and regularized feature selection.

ABSTRACT: Speech emotion classification (SEC) has gained the utmost height and occupied a conspicuous position within the research community in recent times. Its vital role in Human-Computer Interaction (HCI) and affective computing cannot be overemphasized. Many primitive algorithmic solutions and deep neural network (DNN) models have been proposed for efficient recognition of emotion from speech however, the suitability of these methods to accurately classify emotion from speech with multi-lingual background and other factors that impede efficient classification of emotion is still demanding critical consideration. This study proposed an attention-based network with a pre-trained convolutional neural network and regularized neighbourhood component analysis (RNCA) feature selection techniques for i

SUBMITTER: Akinpelu S 

PROVIDER: S-EPMC10368662 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature