Speech emotion recognition framework and applications in emergency call centers

Nowadays, Emotion recognition using speech signals plays a significant role in various applications including lie detection, security issues, and emergency call centers. Speech emotion recognition can be used to detect such as fear, sadness, and happiness that could help in making effective decisions. Speech emotion recognition is used to extract emotions from speech signals. Our proposed system has three main stages which are feature extraction, Feature selection, and classifications. The main goal of this work is to design a framework for call center applications using speech emotion recognition. The framework will start with preprocessing step in which noise will be removed from the input speech signal using filters. Next, we applied Mel Frequency Cepstral Coefficients (MFCC) algorithms to extract the features. In the third stage Support Vector Machine classifier is utilized to identify the emotional state that was extracted from our proposed features.