# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 1 Introduction  
Spoken language processing combines knowledge from the interdisciplinary area of natural language processing, cognitive sciences, dialogue systems, and information access. Speech Emotion Recognition (SER) and text-to-speech synthesis (TTS), including voice and style conversion, as part of human-machine spoken dialogue systems correspond to certain cognitive aspects underlying the human language processing system [1]. In the last few decades, there has been growing interest in developing human-machine interfaces that are more adaptive and responsive to a user's behavior [2]. In that sense, the use of emotion in speech synthesis and recognition of emotion in speech takes an important place in attempts to improve naturalness of human-machine interaction (HMI) [3]. As to TTS, different applications such as smart environments, virtual assistants, intelligent robots, and call centers have set requirements for different speech styles identified with corresponding emotional expressions [4]. Recognition of emotions in HMI is not restricted to speech analysis only, but also image analysis (facial expression recognition, eye-tracking data) and physiological signals (pulse rate, skin conductance, facial electromyography, electroencephalography (EEG) signal) [5]. Emotion recognition in spoken dialogue systems such as call centers provides a possibility to respond to callers according to the detected emotional state or to pass control over to human operators [2; 6; 7; 8]. In the SER research, two main approaches are utilized in describing the emotional space. The first approach describes the emotional space with a finite number of prototypical emotions according with categorical emotion model. The second approach uses dimensions (typically arousal and valence) to determine possible emotional states in the space defined by chosen dimensions. The latter approach corresponds to dimensional emotion models. Dimensional emotion models mostly use two or three dimensions (e.g., valence, arousal, and sometimes dominance) to describe the emotional space in which the emotional variability is to the greatest extent determined by the first two dimensions and thus used as a basis for research in the field of SER [9]. The valence dimension describes the pleasantness of emotion and ranges from positive (e.g., joy) to negative (e.g., anger). The arousal dimension indicates the level of activation during some emotional experience and it ranges from passive (e.g., sleepiness) to active (e.g., high excitement). The position of some basic, categorical emotions in the valence-arousal space is shown in Figure 1.  
The dimensional models allow using emotional categories (appropriately positioned in a two-dimensional emotional space) among which it is possible to determine a distance metric [10]. [PERSON] et al. utilized four dimensions (namely, valence, arousal, sense of control, and ease in achieving a goal) to describe user's emotional state while interacting with an electronic game [11]. [PERSON] proposed a procedure to obtain new mappings with mapping matrices for estimating the dimensions of a valence-arousal-dominance model from [PERSON]'s six basic emotions [12]. The procedure, as well as the proposed metrics, might be used, not only in evaluation of the mappings between representation models, but also in a comparison of emotion recognition and annotation results. Emotion valence classification in self-assessed affect challenge is reported in [13]. Detection of a degree of speaker's sleepiness can help recognizing his/her emotional arousal as well [14]. Sometimes both approaches, emotion category and valence-arousal classification, are utilized for comparison, as in the INTERSPEECH Emotion Sub-Challenge on acted speech corpus [15]. In a situation when all call center operators are busy and unable to answer a new call, the system puts that call on hold regardless of its urgency.