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This paper is mainly concerned with speech based emotion recognition. The main work is concerned with Gaussian Mixture Model (GMM model) which allows training the desired data set from the databases. GMM are known to capture distribution of data point from the input feature space, therefore GMM are suitable for developing emotion recognition model when large number of feature vector is available. Given a set of inputs, GMM refines the weights of each distribution through expectation-maximization algorithm. Once a model is generated, conditional probabilities can be computed for test patterns (unknown data points).
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