# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Voice-Based Emotion Recognition
In order to merge the power of various classifiers during the process of recognizing various emotional states, a confidence-based fusion technique was devised. Three studies were carried out to identify emotions using the IEMOCAP corpus. The weighted accuracy of the proposed fusion system was 57.1%, while the unweighted accuracy was 58.3%. These results are substantially higher than those of each individual classifier [70]. [PERSON] et al. sought to employ deep neural networks to identify the emotions expressed in human speech. The MFCC, which stands for Mel-frequency cepstral coefficient, is obtained by extracting features from raw audio data and then training them into a DNN. The trained neural network is assessed using labeled emotion speech audio, and the accuracy of emotion recognition is measured. The quantity of neurons and layers is modified in accordance with the accuracy rate of the MFCCs. An optimal network is utilized to introduce and validate a bespoke database. The optimal setup for speech emotion recognition (SER) consists of 13 MFCCs, 12 neurons, and two layers for three emotions. For four emotions, the recommended configuration is 25 MFCC, 21 neurons, and four layers. These settings result in an overall recognition rate of 96.3% for three emotions and 97.1% for four emotions. This research represents a focal point for the recognition of emotions in speech because of its difficult nature but exciting potential for the future. The optimal setup for SER involves using 13 MFCC features, 12 neurons, and two layers to classify three emotions. For four emotions, it is recommended to use 25 MFCC features, 21 neurons, and four layers [71]. [PERSON] et al. introduce a sophisticated framework based on deep recurrent neural networks to identify and forecast the degree of depression using speech. The method captures both low-level and high-level audio characteristics from recordings, enabling the prediction of Patient Health Questionnaire scores and a binary classification of depression diagnosis. The framework demonstrates superior performance compared to the most advanced techniques on the DAIC-WOZ database, achieving an overall accuracy of 76.27% and a root mean square error of 0.4 [72]. [PERSON] and [PERSON] offer a voice emotion recognition model that utilizes an enhanced deep belief network and replaces conventional activation functions with rectified linear units (Relu). This model has the ability to automatically identify six different emotions: anger, fear, joy, tranquility, sorrow, and surprise. It achieves a recognition rate of 84.94%, which is higher than the recognition rates achieved by classic DBN and BP models [73]. [PERSON] et al. presented a novel emotion recognition system that utilizes a Taylor series-based deep belief network (Taylor-DBN). The method eliminates background noise from speech and extracts characteristics such as the tone power ratio and MKMFCC values. Experiments conducted on various datasets demonstrate negligible deviations in performance across many areas and languages. The model surpasses previous models in terms of the false rejection rate, false acceptance rate, and accuracy [74]. [PERSON] et al. presented a novel approach for feature extraction by employing deep neural networks to automatically extract emotional elements from speech inputs. The method employs a stack of five deep belief networks and integrates numerous consecutive frames to generate high-dimensional features. The characteristics are subsequently entered into a nonlinear SVM classifier, yielding a speech emotion recognition multiple classifier system with an 86.5% recognition rate [75]. [PERSON] et al. proposed a technique to improve the classification recall of a deep Boltzmann machine used for emotion recognition from voice. The approach entails partitioning the input data into distinct subsets, training each subset separately, and sub sequently evaluating the outcomes using SVM classifiers and the DBM algorithm. This approach is appropriate for datasets that are not evenly distributed [76]. [PERSON] et al.