An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm

At present, there are several communication tools employed to express human emotions. Among the numerous modes of communication, speech is the most predominant one for communicating with people effectively and efficiently. Speech emotion recognition (SER) plays a significant role in several signal processing applications. However, in both feature selection (FS) methods and reliable classifiers, determining their appropriate features has emerged as challenges in identifying the emotions expressed in Indian regional languages. In this work, a novel SER framework has been proposed to classify different speech emotions. Primarily, the proposed framework utilizes a preprocessing phase so as to alleviate the background noise and the artifacts present in input speech signal. Later on, the two new speech attributes related to energy and phase have been integrated with state-of-the-art attributes for examining speech emotion characteristics. The threshold-based feature selection (TFS) algorithm has been introduced to determine the optimal features by applying a statistical approach. An Indian regional language called Tamil Emotional dataset has been created for examining the proposed framework with the aid of standard machine learning and deep learning classifiers. The proposed TFS technique has been more suitable for Indian regional languages since it exhibits a superior performance with 97.96% accuracy compared to Indian English and Malayalam datasets.