A comparison of Bayesian and HMM based approaches in machine learning for emotion detection in native Kannada speaker

We build an emotion detection system based on Bayesian multivariate modeling and compare the same with the one based upon Hidden Markov Modeling (HMM) scheme. Both the systems were built upon probabilistic pattern recognition and acoustic phonetic recognition. Since our native language is Kannada, one of very rich South Indian language, we have used 4 Emotions uttered in Kannada to train and test the schemes. Since Mel Frequency Cepstral Coefficients (MFCC) are well known acoustic features of speech [1][2][4], we have used the same in speech feature extraction. Finally performance analysis of these models in terms of Emotion Error Rate (EER) justifies the fact that Dynamic modeling using HMM yields better results over other modeling schemes and can be used in developing Automatic Speech Recognition systems.