# Emotion Recognition Method for Call/Contact Centre Systems
## 2 Related Work
When it comes to emotion recognition in audio files, some solutions based on the following classifiers are known: SVM (support vector machine), HMM (hidden Markov model), GMM (Gaussian mixture model), kNN (k-nearest neighbour), ANN (artificial neural network), DT (decision trees) and fuzzy classifiers. Recently, solutions based on DNN (deep neural networks), including CNN (convolutional neural networks), have become most popular. The second part of Table 1 presents the characteristics of selected emotion recognition solutions for audio conversations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Solution** & **Proposed Approach** & **Results** & **Ref. ** \\ \hline \hline  & \multicolumn{3}{c}{**Text channel**} \\ \hline
1. & Sentiment analysis of Twitter data & SVM model using the following descriptors: POS-tagging, tree kernel, semi features. & A validation level of about 56–60\% was achieved, the model recognizes three groups of emotions: positive, negative, and neutral. & [18] \\ \hline
2. & MTC problem solution & The kNN algorithm combined with profile classifiers with improved performance. & A validation rate of approximately 67\% was achieved. The model considers the relevance of the documents to the case, which are divided into two categories: positive and negative. & [36] \\ \hline
3. & Sentiment classification based on SVM and ANN & SVM and ANN models using the TF/IDF descriptor. & For ANN, the validation level is about 76–90\%, classifier, the validation level is about 74–89\%, Models recognize emotions: positive and negative. & [37] \\ \hline
4. & Twitter sentiment analysis based on ordinal regression & The following network types were tested: SVR, DT, RFC. The TF/IDF descriptor was used. & For DT, the validation level is about 92\%, for RFC about 83\% and for SVR-82\%. The models listed above recognize positive and negative emotions. & [30] \\ \hline
5. & Sentiment representation model for Twitter analysis & The following network types were tested: SVM, RFC, CNN, and LSTM, and the Bayes classifier NBC. The QSR descriptor was used. & For the LSTM and CNN networks, the validation level is about 78\%, the other solutions had validation levels in the range of about 64–65\%. & [38] \\ \hline
6. & Twitter text analysis using NBC, kNN and SVM & The following classifiers were tested: NBC, kNN, and SVM. They use the TF/IDF descriptor. & The models listed above recognize emotions: positive and neutral. & [39] \\ \hline
7. & Multi modal dialog act classification & DNN based model using GloVe vectors as a descriptor. & A validation rate of 39–65\% was achieved depending on the training dataset used. The model recognizes the following 8 emotions: sadness, anger, fear, happiness, surprise, disgust, frustration, neutrality. & [40] \\ \hline
8. & Attention based word embeddings & The ABC algorithm with SVM classifier was used with the objective of maximizing classification accuracy. ATVs were used as descriptors. & A validation rate of 82–96\% was achieved depending on the training dataset used. The model recognizes positive, negative, and neutral emotions. & [41] \\ \hline  & \multicolumn{3}{c}{**Voice channel**} \\ \hline
1. & Emotion detection through speech & CNN network was tested. MFCC were used as descriptors. & For the CNN model, the best performance was achieved (at about 83\%), the model recognizes the following types of emotions: anger, disgust, fear, happiness, sadness, and surprise & [42] \\ \hline
2. & Speech emotion recognition based on rough set and SVM & The training data for the SVM model is a total of 13 parameters, including three from the energy features group, five pitch features, four formant features, and speech rate. & A validation level of approximately 78\% was achieved, the model recognizes the following three types of emotions: anger, happiness, sadness, There is variation by gender. & [43] \\ \hline
3.