Influence of emotion distribution and classification on a call processing for an emergency call center

Abstract

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.