SPEECH EMOTION RECOGNITION

Abstract

Speech Emotion Recognition, abbreviated as SER, is the act of attempting to recognize human emotion and affective states from speech. This is capitalizing on the fact that voice often reflects underlying emotion through tone and pitch. This is also the phenomenon that animals like dogs and horses employ to be able to understand human emotion. SER is tough because emotions are subjective and annotating audio is challenging.The main objective of this SER, recognizing the emotional state of the speaker from his/her speaker. Various emotions like happiness, sadness, anger, etc. can be recognized. SER uses speech processing to recognize the emotional state. Speech Processing is one of the important branches of digital signal processing and finds applications in Humancomputer interfaces, Telecommunication, Assistive technologies, Audio mining, Security, and so on. Speech Emotion Recognition is important to have a natural interaction between human beings and machines. In SER, the emotional state of a speaker is extracted from his/her speech. The acoustic characteristic of the speech signal is Feature. Feature extraction is the process that extracts a small amount of data from the speech signal that can later be used to represent each speaker. Many feature extraction methods are available and Mel Frequency Cepstral Coefficient (MFCC) is the commonly used method. In this project, speaker emotions are recognized using the data extracted from the speaker's voice signal. Mel Frequency Cepstral Coefficient (MFCC) technique is used to recognize the emotion of a speaker from their voice.