Leveraging machine learning based human voice emotion recognition system from audio samples

• Main Authors: Anbalagan, Bhuvaneswari, Radhika, R., Jayanthi, R., Prabha K. P., Rama
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Emotion recognition; Emotions; Exhibitions; Human-computer interface; Machine learning; Speech recognition; Support vector machines; Voice recognition
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0101448

Emotion Recognition through voice tests is a new exploration subject in the Human-Computer Interaction (HCI) field. The requirement for it has emerged for an all the more simple correspondence interface among people and PCs since PCs have become the fundamental piece of our lives. To accomplish this objective, a PC would need to have the option to separate its present circumstance and react contrastingly relying upon that specific perception. The proposed human identification includes understanding a client’s passionate state and to make the human-PC cooperation more regular, the principle objective is that the PC ought to have the option to perceive the enthusiastic conditions of people in equivalent to a human does. The proposed framework focuses on the recognizable proof of fundamental enthusiastic states like indignation, happiness, nonpartisan, and pity from human voice tests. While characterizing various speech recognitions, highlights like MEL frequency cepstral coefficient and energy are utilized. The proposed strategy depicts and thinks about the exhibitions of learning multiclass Support Vector Machine (SVM) , Random Forest (RF) and their mix of speech recognition acknowledgment. The MFCC and SVM algorithm proves to be an efficient no-regret online algorithm which detects the speech recognition with average classification accuracy of 89% which is reasonably acceptable.