# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 2 Materials and Methods
### Comparison of the SER Results with Other Studies  
In general, it is a difficult task to objectively compare results of one SER research with other results reported in literature. This is due to a high diversity of research approaches to SER, regarding speech emotion corpora, the extracted feature set, classification methods and additional experimental settings (e.g., speaker-dependent or speaker-independent tests, cross-validation method applied). Regarding acted speech, two corpora have been used in plenty of research: Berlin Emotional Speech Database (Emo-DB) containing the total of 535 sentences uttered by 10 actors (5 male, 5 female) in seven emotional states, and Danish Emotional Speech Database (DES) containing the total of 419 utterances portrayed in five emotional states by 4 actors (2 male, 2 female) [28; 37]. In the research by [PERSON] et al. the proposed 3 DEC classification was tested on all three corpora (Emo-DB, DES, GEES), and the best results were achieved on the GEES corpus [37]. It can be explained by the fact that the GEES contains more samples available for training the classifier than other two corpora, and by the fact that the overall human recognition accuracy reported for GEES is 94.7%, against 86.1% for Emo-DB and 67.3% for DES. The overall human accuracy reflects the distinction degree of the acoustic representation of basic emotions in a corpus, which is very high for the GEES corpus. In our earlier study [42], the comparison of basic emotion classification in valence-arousal space was made on the Emo-DB and the GEES corpora. The mapping of basic emotions into three classes along the valence axis (positive, neutral, and negative), and three classes along the arousal axis (high, neutral, and low) was performed. The recognition results along the arousal axis were above 90% for both corpora. The average recognition results along the valence axis were 83.2% for the GEES and 76.9% for Emo-DB. It is in line with the findings showing that arousal discrimination tasks, based on acoustic features, achieve higher recognition rates than valence discrimination tasks [28]. We consider the GEES with 1740 utterances portrayed in 5 emotional states by 6 actors as a suitable and adequate basis for SER research. Also, taking into account that Serbian is still an under-resourced language, there are far less available emotional speech data and the corresponding research for Serbian (GEES is the only one emotional speech corpus accessible for research purposes) even in comparison with other Slavic languages like Russian [43], Czech [44], etc. A comparative analysis of our results and the results of some other SER studies conducted on the GEES corpus was performed. Due to the fact that this is a rather small corpus in Serbian, it was not a subject of much research. Two SER researches on the GEES corpus were found to be compared with our results. The first study for comparison is by [PERSON] et al. [37], who proposed a hierarchical classification technique using SVM for binary emotion classification on every level. As to feature extraction they decided to apply a \"brute force\" approach and 6552 acoustic features for each utterance. The extracted feature vector included 56 low-level descriptors (among which is pitch, energy, spectral energy, MFCCs) and 39 statistical functionals applied to these LLDs and their first and second derivatives. In the experiments three acted databases were used: the Danish Emotional speech (DES), the Berlin database (Emo-DB) and the Serbian GEES database, and one spontaneous database (Aibo corpus). The proposed hierarchical classification, called 3 DEC, is based on input data in such a way that input data and their confusion plots determine the hierarchy of the proposed classification scheme. They used both speaker-dependent and speaker-independent approaches for SVM-based model training and testing. We present only results of speaker-dependent tests as to be able to make a comparison with our results.