# Emotion Recognition Method for Call/Contact Centre Systems
## 6 Results
### Voice Channel  
Table 11 summarizes the test results obtained for the five-fold cross validation process obtained for the audio conversation base designated as DBV1. The research uses the classifiers selected in Section 2, namely: CNN, kNN, LDA and SVM.  
The average values of the individual metrics obtained for the voice channel range from 60% to 80%. The weakest results were obtained using the LDA classifier, while the best results were obtained for CNN. The following speech signal descriptors were used in this test: F\({}_{0}\) MFCC, Jitter and Shimer. The obtained classification results are satisfactory for the applicability of the proposed method in the CC industry. The standard deviation values confirm the good stability of the prepared models. Table 12 summarizes the results obtained in the method verification experiment for emotion recognition in CC voice channels. For this purpose, the conversation base designated as DBV2 was used. As for the text data, the agent and client channels were specified in the experiment.  
Analysis of the results of the experiments performed on the verification sample confirmed the effectiveness of the developed solution. In this case, the CNN classifier did a very good job of identifying the agents' emotions. In addition, the results for emotion recognition in voice channels are clearly better than for the text channel. This is due to the nature of the audio signal, which contains much more information that allows the identification of individual types of emotions using artificial intelligence algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{8}{c}{**Classifier**} \\ \cline{2-10}  & **ABC** & **ANN** & **DT** & **kNN** & **NBC** & **RFC** & **SVM** \\ \hline \multicolumn{10}{c}{Agent} \\ \hline ACC & 0.64 & 0.60 & 0.53 & 0.63 & 0.29 & 0.64 & **0.75** \\ WAP & 0.69 & 0.72 & 0.72 & 0.68 & 0.74 & 0.73 & **0.72** \\ WAF1 & 0.66 & 0.65 & 0.60 & 0.65 & 0.26 & 0.67 & **0.73** \\ \hline \multicolumn{10}{c}{Client} \\ \hline ACC & 0.53 & 0.53 & 0.48 & 0.50 & 0.32 & 0.51 & **0.60** \\ WAP & 0.51 & 0.57 & 0.51 & 0.50 & 0.56 & 0.51 & **0.50** \\ WAF1 & 0.51 & 0.54 & 0.49 & 0.49 & 0.32 & 0.50 & **0.54** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Verification of the method based on the DBT2 database.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**kNN**} & \multicolumn{2}{c}{**LDA**} & \multicolumn{2}{c}{**SVM**} \\ \cline{2-10}  & **ACC** & **WAP** & **WAF1** & **ACC** & **WAP** & **WAF1** & **ACC** & **WAP** & **WAF1** & **ACC** & **WAP** & **WAF1** \\ \hline
1 & 0.78 & 0.79 & 0.78 & 0.71 & 0.66 & 0.68 & 0.66 & 0.58 & 0.61 & 0.72 & 0.70 & 0.67 \\
2 & 0.88 & 0.88 & 0.87 & 0.70 & 0.69 & 0.68 & 0.67 & 0.62 & 0.63 & 0.71 & 0.64 & 0.65 \\
3 & 0.86 & 0.86 & 0.86 & 0.69 & 0.66 & 0.65 & 0.62 & 0.64 & 0.57 & 0.69 & 0.63 & 0.63 \\
4 & 0.85 & 0.85 & 0.84 & 0.69 & 0.69 & 0.66 & 0.66 & 0.58 & 0.61 & 0.64 & 0.74 & 0.57 \\
5 & 0.78 & 0.78 & 0.78 & 0.71 & 0.69 & 0.69 & 0.66 & 0.57 & 0.60 & 0.70 & 0.76 & 0.65 \\ Av & 0.83 & 0.83 & 0.83 & 0.70 & 0.68 & 0.67 & 0.65 & 0.60 & 0.60 & 0.70 & 0.69 & 0.63 \\ Std & 0.05 & 0.04 & 0.04 & 0.01 & 0.02 & 0.02 & 0.02 & 0.03 & 0.02 & 0.03 & 0.05 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Cross validation test results for the voice channel.