Speech Emotion Recognition System using SVD algorithm with HMM Model

From many years there has been gaining interest in the field of SER using Matlab. SER states the emotional state by analyzing the input speech. SER has a simple pattern and also including feature extraction, feature matching, classification and database. Here, from the input speech by using algorithm features are extracted by using some models the feature matching takes place. By this process we use to analyze the characteristics of the input speech signal. Hence, the system recognize the state of emotion. The system states some the of emotions: Angry, Boredom, Anxiety, Disgust,Happiness, Neutral, Sadness. The main purpose of this paper is to give survey on two of the algorithms using HMM model with different speech emotion databases. There are several audio features for extracting are available. And also various classifiers are available. The most popular models are Hidden Markov model(HMM), Vector Quantization(VQ), Gaussian Mixture model(GMM), Deep Neural Networks (DNN), Neural Networks(NN) and Artificial Neural Networks(ANN). There are several speech emotion databases included Berlin database. Hence, we reviewed some of the models will be discussed in this paper.