Emotion recognition from Assamese speeches using MFCC features and GMM classifier

This paper presents a method based on Gaussian mixture model (GMM) classifier and Mel-frequency cepstral coefficients (MFCC) as features for emotion recognition from Assamese speeches. For training and testing of the method, data collection is carried out in Jorhat (Assam, India), which consisted of acted speeches of one short emotionally biased sentence repeated 5 times with different styles by 27 speakers (14 Male and 13 female) for training and one long emotional speech by each speaker for testing. The experiments are performed for the cases of (i) text-independent but speaker-dependent and (ii) text-independent and speaker-independent.