# Emotion Recognition Method for Call/Contact Centre Systems
## 3 Research Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Solution** & **Proposed Approach** & **Results** & **Ref.** \\ \hline \multirow{4}{*}{3.} & Emotion recognition based on bottleneck acoustic features and lexical features & A DNN network and the F0, MFCC, 40 mel filterbank energies parameters descriptors were used to detect emotions based on acoustic features. In terms of emotion classification from text, the DNN network was also used with word2 vec and ANEW descriptors. & A validation level of 74\% was achieved, DNN model fusion recognizes anger, joy, sadness, and neutrality. & [48] \\ \hline \multirow{4}{*}{4.} & \multirow{4}{*}{Multimodal emotion recognition network with personalized attention profile} & For emotion detection based on acoustic features, a BLSTM network and 45-dimensional vector consisting of MFCC features, F0, zero cross ratio along with the first and second derivatives of MFCC were used. Meanwhile, BLSTM using the GloVe method was also used to detect emotion in text. & [49] \\ \hline \multirow{4}{*}{5.} & \multirow{4}{*}{Speech emotion recognition based on attention weight correction using word level measure} & The BLSTM network and the following descriptors were used to detect emotions based on acoustic features: MFCC, constant transform and average fundamental frequency. In terms of emotion classification based on text, the BLSTM network was also used with the BERT model. & A validation level of about 76\% was achieved. & [49] \\ \hline \multirow{4}{*}{5.} & \multirow{4}{*}{Speech emotion recognition based on attention weight correction using word level measure} & The BLSTM network and the following descriptors were used to detect emotions based on acoustic features: MFCC, constant transform and average fundamental frequency. In terms of emotion classification based on text, the BLSTM network was also used with the BERT model. & [50] \\ \hline \multirow{4}{*}{5.} & \multirow{4}{*}{Ref.–references; TF/IDF—term frequency inverse document frequency; SVR—support vector regression; QSR—quantum-inspired sentiment representation; ATV—attention vector; PCA—principal component analysis; LDA—linear discriminant analysis; ANEW—affective norms for English words.} & \multirow{4}{*}{[51]} \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The purpose of the above papers was to determine the potential use of the developed algorithms as components of the emotion recognition method proposed in the paper. The illustrative block diagram of the test methodology is presented in Figure 1.  
In the initial phase of the research work, databases were created for text conversations and audio recordings. All data represented actual client-agent conversations in a large contact centre system operating on a commercial basis. The prepared databases are marked in the subsequent part of the paper as: DBT1, DBT2, DBV1, DBV2 as well as DBVT1 and DBVT2. A detailed analysis for the described datasets is presented in the paper [54], while Table 2 summarizes their most salient features. The data collected in the DBT1 and DBV1/DBVT1 databases were further evaluated by experts who extracted emotionally charged fragments from individual conversations. Three independent judges participated in assessing the emotional states present in the collected databases in each part of the work. The evaluation of the text data was performed by a linguist, a psychologist working on emotion issues, and an engineer designing algorithms to detect emotions in text channels. In contrast, the evaluation of the audio files was performed by two psychologists working on emotion issues and an engineer designing algorithms