Speech based human emotion recognition using MFCC

Speech is a complex signal consisting of various information, such as information about the message to be communicated, speaker, language, region, emotions etc. Speech Processing is one of the important branches of digital signal processing and finds applications in Human computer interfaces, Telecommunication, Assistive technologies, Audio mining, Security and so on. Speech emotion recognition is important to have a natural interaction between human being and machine. In speech emotion recognition, emotional state of a speaker is extracted from his or 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 paper, speaker emotions are recognized using the data extracted from the speaker voice signal. Mel Frequency Cepstral Coefficient (MFCC) technique is used to recognize emotion of a speaker from their voice. The designed system was validated for Happy, sad and anger emotions and the efficiency was found to be about 80%.