# Emotion Recognition Method for Call/Contact Centre Systems
###### Abstract  
Nowadays, one of the important aspects of research on call/contact centre (CC) systems is how to automate their operations. Process automation is influenced by the continuous development in the implementation of virtual assistants. The effectiveness of virtual assistants depends on numerous factors. One of the most important is correctly recognizing the intent of clients conversing with the machine. Recognizing intentions is not an easy process, as often the client's actual intentions can only be correctly identified after considering the client's emotional state. When it comes to human-machine communication, the ability of a virtual assistant to recognize the client's emotional state would greatly improve its effectiveness. This paper proposes a new method for recognizing interlocutors' emotions dedicated directly to contact centre systems. The developed method provides opportunities to determine emotional states in text and voice channels. It provides opportunities to explore both the client's and the agent's emotional states. Information about agents' emotions can be used to build their behavioural profiles, which is also applicable in contact centres. In addition, the paper explored the possibility of emotion assessment based on automatic transcriptions of recordings, which also positively affected emotion recognition performance in the voice channel. The research used actual conversations that took place during the operation of a large, commercial contact centre. The proposed solution makes it possible to recognize the emotions of customers contacting the hotline and agents handling these calls. Using this information in practical applications can increase the efficiency of agents' work, efficiency of bots used in CC and increase customer satisfaction. all centre; contact centre; emotion recognition; chatbot; voicebot; AI +
Footnote †: journal: Accepted: 27 October 2022  
From the point of view of building intelligent bots that automatically conduct CC hotline conversations, the sentiment-sensitive approach is not fully sufficient. Looking at practical applications from another perspective, knowledge about the emotions of hotline users is also important for supervisors who evaluate both the work of individual agents and the effectiveness of conducted campaigns. For example, when an agent expresses negative emotions, it could result in a direct review and possibly in the call being transferred to another person. In current environments, there is no such functionality and call verification is usually performed randomly, which is neither sufficient nor optimal. Another aspect of using the knowledge of emotions surrounding CC conversations is the ability to build behavioural profiles of both the agent and the client. Identifying behavioural profiles would provide new opportunities for matching interlocutors. Currently, CC solutions that match CC interlocutors are typically based on knowledge of the agent's skills. This approach is called skills-based routing [7]. Knowledge of the behavioural profile that takes into account the emotional characteristics of interlocutors could help improve many of the KPIs (key performance indicators) used as metrics in CC systems. Considering the above, it should be noted that an important problem from the point of view of the functionality of CC systems is the lack of a target emotion recognition method dedicated directly to these systems, which can be easily integrated with their various functionalities to improve performance. The identified deficiencies in this area were the motivation for the authors to develop an intelligent method for recognizing the emotions of clients and agents communicating in both text and CC voice channels.