# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 2 Materials and Methods
### Classification Methods  
A recent survey in the field of SER provided an overview of traditional classifiers and deep learning algorithms applied for SER [35]. Among traditional classifiers, they listed Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), Decision Trees (DT), k-Nearest Neighbor (kNN), k-means, and Naive Bayes Classifiers, concluding that there is no generally accepted machine learning algorithm used in this field. Recently, the focus on research changed direction towards Deep Neural Networks (DNN), with most widely used Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). For the purpose of speech emotion classification in this study, Linear Discriminant Classifiers (LDC) and kNN are taken into account due to their simplicity, efficiency and low computational requirements. LDCs and kNN classifiers have been used since the very first studies and turned out to be quite successful for both acted and spontaneous emotional speech [19]. [PERSON] et al. used a weighted kNN classifier for the classification task of four emotions (anger, sadness, joy and neutral) contained in the SROL emotion corpus utilized in their research [36]. In all our experiments, 10-fold partitioning of the data set was used to estimate the recognition accuracy of a particular classifier. Training and test sets included utterances from all six speakers, so these results belong to speaker-dependent experiments. Although \"speaker-independent\" experiments (e.g., leave-one-speaker-out) are possible on the GEES with 6 speakers (for example, the results reported by [37]), we decided to perform speaker-dependent tests in order to train classifier with more samples belonging to different speakers. In such a way, the acoustic variability present in the feature space is better modelled providing better prediction ability even when tested with an unknown speaker. Indeed, the accuracies obtained in speaker-independent cross-validation tend to be lower than the accuracies obtained in speaker-dependent cross-validation [37], but not significantly [38]. The first considered classifier is the linear Bayes classifier with the underlying assumption that classes are modeled by Gaussian densities and equal covariance matrices. Maximum likelihood estimates of Gaussian density parameters are used. As to the linear Bayes classifier, the average recognition accuracy achieved in our emotion classification experiments on the GEES corpus was 91.5% [32]. Joy was recognized with 84.2% and anger with 88.8% recognition rate. Class recognition rates for fear, neutral and sadness in the case of linear Bayes classifier were 92.5%, 97.1% and 94.8%, respectively. Table 1 shows a normalized confusion matrix for the linear Bayes classifier applied on the GEES corpus. From Table 1, it could be noted that sadness is misrecognized as a neutral state in 4% of test samples and fear is confused with neutral state in 3.2%. Neutral state has the highest recognition rate, thus its misclassification as fear and joy is about 1%. Anger and joy have lower recognition rates due to the problem of mutual misclassification, about 11% of anger test samples is recognized as joy and almost 15% of joy is misrecognized as fear. For the second classifier, the kNN classifier is used as a very intuitive method that classifies unlabeled examples based on their similarity to examples in the training set. It implicitly involves non-parametric density estimation, which leads to a very simple approximation of the linear Bayes classifier. Employing high dimensionality feature vectors, dimensionality reduction is sometimes applied in order to improve classification results, as in [39], where a speaker-penalty graph learning is proposed to penalize the impact of different speakers. Due to the fact that the recognition accuracy of the kNN classifier is affected by the high dimensionality of feature set, linear discriminant analysis (LDA) feature reduction has been applied on feature set [40].