Gammatone-Frequency Cepstral Coefficients Based Fear Emotion Level Recognition System

Abstract

Emotions represent affective states that induce alterations in behavior and interactions within one's environment. An avenue for discerning human emotions lies in the realm of speech analysis. Empirical evidence indicates that 1.6 million Indonesian teenagers grapple with mental anxiety disorders, characterized by sensations of fear or ambiguous vigilance. This work endeavors to devise a tool for discerning an individual's emotional state through voice processing, focusing particularly on fear emotions stratified into three levels of intensity: low, medium, and high. The proposed system employs Gammatone-Frequency Cepstral Coefficients (GFCC) for feature extraction, leveraging the efficacy of its gamma filter in reducing noise. Furthermore, a Random Forest (RF) Classifier is integrated to facilitate the recognition of fear's emotional intensity in speech signals. The system is deployed on a Raspberry Pi 4B and establishes a Bluetooth connection using the RFCOMM communication protocol to an Android application, presenting the classification results. The outcomes reveal that the Signal-to-Noise Reduction achieved through GFCC extraction surpasses that of Mel-Frequency Cepstral Coefficients (MFCC). In terms of accuracy, the implemented recognition system for fear emotion levels, employing GFCC extraction and Random Forest Classifier, attains a commendable accuracy of 73.33 %.