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EMOTION RECOGNITION FROM SPEECH WITH GAUSSIAN MIXTURE MODELS AND VIA BOOSTED GMM
B. Meenapreethi, Deepika G. Krishnan* and Sivaranjani G.
Speech has several endowment features such as naturalness and efficient, which makes it as winsome interface medium. It is possible to express emotions and attitudes via speech. In human machine interface application emotion recognition from the speech signal has been prevailing topic of research. Speech emotion recognition is an important issue which affects the human machine intercommunication. Automatic recognition of human emotion in speech angles at recognizing the primitive emotional state of a speaker from the speech signal. Gaussian mixture models (GMMs) and the scintilla error rate classifier (i.e. Bayesian optimal classifier) is embraced and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic visage and their parameters are outlined by the expectation maximization (EM) algorithm based on a training data set. Then, classification is performed to minimize the classification error w.r.t. the judged class conditional distributions. This method is called the EM-GMM algorithm. In this paper, we discuss about boosting algorithm for reliably and accurately estimating the classconditional GMMs. The resulting algorithm is named the Boosted-GMM algorithm. This speech recognition experiment shows better results than the prior algorithms available update. This is due to the fact that the boosting algorithm can lead to more scrupulous estimates of the class-conditional GMMs, namely the class-conditional dispersions of acoustic features.[Full Text Article]