# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 2 Materials and Methods
### Acoustic Modeling  
The most commonly used acoustic features for SER are: prosodic features (pitch, intensity, duration), cepstral features (MFCC), spectral features (formant position and bandwidth), and occasionally voice quality features (harmonic-to-noise ratio, jitter, shimmer), in line with the studies [19; 20; 21; 22; 23]. The task of finding a robust feature set has led to the idea of applying statistical functionals to low-level descriptors (LLD) and resulted in very large feature vectors containing up to a few thousands of prosodic and spectral features [19]. Recently, new trends in machine learning have been directing research of automatic affect recognition towards end-to-end technique that combines deep, convolutional and recurrent neural networks trained directly on underlying raw audio signal [24; 25]. A proposal of multilevel model based on a combination of LLDs and convolutional recurrent neural network model is given in [26]. Still, a lot of research in the area is based on hand-crafted features that have shown to be robust in many computational paralinguistics tasks such as emotion, autism, accent, addressee, deception, cognitive and physical load detection, and so on [20; 21; 22] (list of the INTERSPEECH Paralinguistics Challenge tasks up to 2019 is available at [[http://www.compare.openaudio.eu/tasks/](http://www.compare.openaudio.eu/tasks/)]([http://www.compare.openaudio.eu/tasks/](http://www.compare.openaudio.eu/tasks/))). [PERSON] et al. introduced the INTERSPEECH 2013 ComParE feature set [20]. It contained 6373 features including energy, spectral, cepstral (MFCC) and voicing related LLDs (pitch, voicing probability, jitter, shimmer), as well as a few LLDs including logarithmic harmonic-to-noise ratio (HNR), spectral harmonicity and psychoacoustic spectral sharpness, etc. This set of hand-crafted acoustic features is still state-of-the-art now [27]. Another more minimalistic feature set proposed in [23] includes prosodic, excitation, vocal tract, and spectral descriptors, obtained by applying functionals to 18 LLDs that give a total of 52 utterance-level features only. [PERSON] et al. used the ComParE feature set for the proposed cascaded normalization. The proposed normalization approach, combining speaker level, value level and feature vector level normalization, has shown a superior performance in the task of cross-corpus acoustic emotion recognition on five corpora recorded in five languages [28]. Utterance level features, obtained through the statistical analysis of prosodic features (pitch, energy), spectral information (formants, spectrum centroid and spectrum cut-off frequency) and cepstral information (mel-frequency bands energy), are extracted to recognize seven basic emotions in Mandarin [29]. While in some studies SER relies on the prosodic and voice quality feature set only [30], and in others on cepstral features only [31], our previous study showed that a combination of both spectral and prosodic features has a higher discrimination capability for speech emotion recognition than prosodic or spectral features used separately [32]. [PERSON] et al. compared and discussed the advantages and usability of hand-crafted and learned representations (an end-to-end system that learns the data representation directly from the raw waveforms) [33]. Their research suggests that hand-crafted features can better generalize to unseen data and can also provide a better robustness to various acoustic conditions in comparison to purely end-to-end systems. The proposed approach to acoustic modeling is based on the statistical analysis of the acoustic feature contours and it is performed in three steps. The openSMILE toolkit [34], used as official baseline for the series of INTERSPEECH Computational Paralinguistics challenges, is used to extract the acoustic feature set. The first step includes the extraction of short-term pitch, energy and 12 MFCC values on a frame basis. Additionally, the voicing probability and the zero crossing rate are calculated for every frame.