Call center operators communicate with callers in different emotional states (anger, anxiety, fear, stress, joy, etc.). Sometimes a number of calls coming in a short period of time have to be answered and processed. In the moments when all call center operators are busy, the system puts that call on hold, regardless of its urgency. This research aims to improve the functionality of call centers by recognition of call urgency and redistribution of calls in a queue. It could be beneficial for call centers giving health care support for elderly people and emergency call centers. The proposed recognition of call urgency and consequent call ranking and redistribution is based on emotion recognition in speech, giving greater priority to calls featuring emotions such as fear, anger and sadness, and less priority to calls featuring neutral speech and happiness. Experimental results, obtained in a simulated call center, show a significant reduction in waiting time for calls estimated as more urgent, especially the calls featuring the emotions of fear and anger.

The article addresses the influence of two aspects on speech emotion recognition utilization for an emergency call center: a frequency of a caller experiencing certain emotional state and classification methods used for speech emotion recognition. In situations when more simultaneous calls in an emergency call center are received, the aim is to detect more urgent callers, e.g. in a life threating situation, and give them priority in a callers' queue. Three different emotion distributions based on the corpora from real-world emergency call centers are considered. The influence of those emotion distributions on the proposed call redistribution and subsequent time savings are reported and discussed. Regarding speech emotion classification, two approaches are presented, namely the linear Bayes classifier and a multilayer perceptron-based neural network. Their recognition results on the corpus of acted emotional Serbian speech are presented and potential application in an emergency call center is discussed.

Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it is necessary to adjust difficulty levels in such a way to keep the game challenging for different players with different playing skills. The mobile app puzzle game Wonderful Animals has been developed consisting of puzzles, find pairs and find differences game (available on the Google Play Store). The game testing was conducted on a group of 40 players by recording game level completion time and conducting a survey of their subjective evaluation of completed level difficulty. The study aimed to find a mechanism to adjust game level difficulty to the individual player taking into account the player’s achievements on previously played games. A pseudo-algorithm for self-learning mechanism is presented, enabling level difficulty adaptation to the player. Furthermore, player classification into three classes using neural networks is suggested in order to offer a user-specific playing environment. The experimental results show that the average recognition rate of the player class was 96.1%.