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.

Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).

The role played by information and communication technologies in human life is growing by the year. During the current pandemic, communication systems have become an essential part of operations of many industries around the world. This has also brought a dynamic development of solutions implemented in Contact Center systems, which ensure quick contact with customers. This paper presents how the pace of the development of Contact Center systems affects optimization of selected Key Performance Indicators (KPIs). It contains a detailed analysis of how successive technologies deployed in the industry, including IVR, chat, WebRTC, Visual IVR, Social Media, MultiChannel, OmniChannel and bots, have affected KPIs. The authors selected essential indicators that are critical to customer service, namely Service Level, Cost per Contact, Customer Satisfaction, Average Handle Time, First Call Resolution, Abandon Rate, Average Waiting Time, and Occupancy Rate. The authors also presented possible trends in the development of the solutions discussed in this paper, including in particular broad possibilities of employing artificial intelligence. AI methods will soon enable equipping Contact Centers with mechanisms for multi-criteria content classification and smart emotion recognition and behavioral profiling methods, and will optimize the process of predicting both inbound and outbound traffic.

Nowadays, one of the key areas of research on contact centre systems is their automation. The main element that influences the possibility of automation of contact centre processes is the call transcription methods implemented by automatic speech recognition (ASR) systems. Such systems enable developing intention recognition methods and, consequently voice bots. The current solutions used in ASR systems for many less popular languages do not guarantee a fully satisfactory transcription quality for hotline voice calls. This is due to the unique characteristics of the sound signal generated there, whose quality parameters differ significantly from those of studio recordings. The paper presents a comparative study of selected speech recognition systems that were additionally supplemented with elements of preprocessing of sound recordings and postprocessing of originally produced transcriptions. As for preprocessing, the following methods were tested: separation of the client and agent channels into two independent signals, training of ASR systems, and audio signal correction. With regards to postprocessing, on the other hand, tests were performed for inarticulate sounds, normalization of standard phrases (e.g. numbers, dates, times, etc.), and identification of close-sounding phrases and foreign language phrases, and lemmatization. Based on the research conducted and the analyses performed, a new method of call transcription intended specifically for contact center systems was proposed. The research conducted for this paper was based on the Polish language model, for which major problems are observed with the quality of automatic contact center call transcriptions.