Refined Feature Vectors for Human Emotion Classifier by combining multiple learning strategies with Recurrent Neural Networks

Abstract

The speech emotion recognition (SER) system categorizes human emotions based on contextual features. However, it is seriously affected during the signal transmission in which the quality of real­time speech processing is degraded in the SER system. This paper presents refined feature vectors for human emotion classifiers based on multiple learning strategies combined with recurrent neural networks (Refine­HE­RNN). It extracts spatial emotional vectors by observing speech signals for contextual feature dependency through the multiple learning (ML) approach. It computes signal interpretation, emotional cues, and input correction by using the skip connection (SC) module in the residual block of the ML strategy. The fused layer is simple to concentrate derived features that support automatic learning of classifying different human emotions. For experimental purposes, standard IEMOCAP and MSP­IMPROV datasets are considered for proposed method validation. Results convey that the proposed method has significant improvement (in terms of percentage closer to 80% higher than the existing CNN result) in the feature recognition and is flexible for real­time implementation in the SER system. Moreover, it can extend to automatic sensing of human emotion with the help of a light weighted RNN framework.