Speech based Emotion Recognition based on hierarchical decision tree with SVM, BLG and SVR classifiers

Emotion Recognition is increasingly becoming an important part of computer vision and robotics. There has been a lot of research and development around this field in the recent times. It is imperative to design emotion recognition systems for real time situations having a considerable rate of accuracy which can find its application in telecommunications, security, etc. This paper discusses a novel design/approach based on a hierarchical decision tree for the GMM means supervector based feature set and using various classifiers viz. SVM, BLG and SVR, to improve the performance of the existing emotion recognition systems. These approaches have been studied on Emo-DB, a German language emotional speech database, yielding about 83% recognition results for closed set based speaker-independent recognition for the optimized method. These results are similar to the results achieved by existing studies on emotion recognition using GMM means supervectors.