# Emotion Recognition Method for Call/Contact Centre Systems
## 7 Conclusions  
This paper proposes a new method for recognizing emotions of interlocutors on a CC hotline. The method assumes recognition of emotional states in both text (chat type) and voice channels. The proposed solution provides capabilities to recognize the emotions of clients contacting the hotline as well as the agents handling these calls, which is important for practical applications. In verification experiments for the prepared classification models, the obtained emotion recognition results for the studied metrics in the text channel even reach values of over 70% for agent utterances and up to 60% for client utterances. In the voice channel, taking into account the conversation transcription and the dictionary integrated in the system, the results for the CNN classifier in both channels reach values above 68%. Taking into account the usual poor audio signal quality found in CC systems, it can be clearly stated that the obtained test results are very satisfactory and show a very high potential for commercial application of the proposed solution. A high level of customer service is becoming increasingly important in the CC industry, and the method proposed by the authors aims to help with these aspects. The solution is expected to improve the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Classifier**} \\ \cline{2-5}  & **CNN** & **kNN** & **LDA** & **SVM** \\ \hline  & & Client & & \\ \hline ACC & **0.68** & 0.53 & 0.48 & 0.62 \\ WAP & **0.80** & 0.81 & 0.73 & 0.76 \\ WAF1 & **0.73** & 0.62 & 0.57 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Verification in voice channel including transcriptions and dictionary. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Classifier**} \\ \cline{2-5}  & **CNN** & **kNN** & **LDA** & **SVM** \\ \hline  & & Agent & & \\ \hline ACC & **0.68** & 0.53 & 0.54 & 0.63 \\ WAP & **0.97** & 0.56 & 0.80 & 0.54 \\ WAF1 & **0.80** & 0.54 & 0.64 & 0.58 \\ \hline  & & Client & & \\ \hline ACC & **0.67** & 0.52 & 0.47 & 0.61 \\ WAP & **0.71** & 0.79 & 0.71 & 0.74 \\ WAF1 & **0.69** & 0.61 & 0.55 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Verification of the method based on the DBV2 database. quality of customer service and optimize relevant KPIs (key performance indicators). These conclusions apply to both the voice channel and the text channel. The developed solution can be easily integrated with different functionalities used in CC systems. This makes it feasible to widely use the developed method in commercial applications. Functionalities in which the emotion recognition method dedicated to the CC industry can be used are, for example, solutions for chatbots and voicebots, methods of creating behavioural profiles of interlocutors (both agents and clients), components responsible for the best possible matching of interlocutors, or in solutions helping to evaluate the work of agents. The method proposed in the paper, after several modifications, can be generalized to applications with a language other than Polish. However, this requires optimization of its selected components. The first of the components requiring optimization is the transcription method implemented as TRANSCRIPTORMODULE, which in the described solution has been adapted to the needs of selected campaign topics conducted in Polish. In this method, first of all, it would be necessary to optimize the algorithms operating on the postprocessing side. Another element requiring modification is the emotional word dictionary component, implemented in the form of the ASR EMOTIONDICTIONARY block. In addition, in order to make the solution even more versatile, relevant retraining processes can be additionally implemented. The task of these solutions should be to constantly and dynamically correct the reference models used.