# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 4 Simulation and Experimental Results  
The research was designed as a set of experiments in a simulated call center receiving a different number of calls simultaneously, i.e., during a short period of time when all operators are busy. The experiments focused on: (i) the redistribution of calls based on emotion label assigned after speech emotion recognition task, and (ii) the evaluation of time period in which the call was put on hold without and after speech emotion recognition was applied for call redistribution. During experiments, the number of simultaneously received calls varied from 3 to 7. In all experiments, prosodic and spectral feature set was used and the linear Bayes classifier and kNN were considered as classification techniques, as described in Section 2. An average waiting time, without and with the redistribution, for each emotional state is evaluated as an average value of waiting time obtained for 50 experimental iterations in the simulated call center with one ideal active human-operator (a human factor is not considered). This procedure is repeated for each experimental setting (3, 5, and 7 simultaneously received calls). Waiting time reduction estimate is made under assumptions about underlying distribution of emotions in input calls and distribution of call duration. We assumed that all the emotions had a uniform distribution as well as that call durations were uniformly distributed across the chosen range (from 30 s to 3 min 50 s). The specified range was chosen with the assumption that it is wide enough to take into consideration the duration of shorter, medium, and longer phone calls as well. Thus, the evaluated waiting time after call redistribution may be shorter for every caller proportionally to the number of active operators in the call center. A pseudo-random number generator is used for the generation of emotion labels (random choice of emotion for input call) as well as the generation of input call duration. The order of the calls in queue (the order of the call arrival) has, as in simulation as in real-world call center, the biggest influence on the estimated waiting time which a caller could spend in callers' queue. In our simulations, the order of the call arrival featuring specific emotion is also unknown and thus determined by generated pseudo-random number. Thus, regarding every iteration in simulation, the random number of occurrences of each emotion class with the associated random call duration, and finally random order of calls (emotions) in callers' queue, jointly influence the variations of estimated average waiting time, before and after call redistribution. Additionally, the recognition rate of some emotional state has an influence on the average waiting time after call redistribution. Simulation of call redistribution in a call center is explained on an experimental example for three simultaneously received calls. Each call is represented by one utterance in the GEES corpus. Firstly, the vector of randomized emotion labels for three input calls was generated. According to the input emotion label vector, three utterances belonging to chosen emotion classes were randomly (regarding a speaker) selected from the corpus and provided as an input to SER. As an initial part of the simulation, duration of a call, generated as a random value between 30 s and 3 min 50 s, was appended to each of these utterances. Knowing the initial order of the simulated calls (determined with input emotion label vector), the initial waiting time in the caller's queue is calculated for each caller as a sum of call duration for all preceding callers in the queue. Every caller is represented with input utterance determined with input emotion label. Thus, initial waiting time for every emotion class is evaluated. Secondly,based on the classifier output each input utterance gets one of the five emotion labels, thus output emotion label vector is obtained. Given the output emotion label, calls are redistributed according to the priority vector.