# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Voice-Based Emotion Recognition
The suggested approach has been tested extensively on CASIA, eNTERFACE, and GEMEP emotion corpora, and the results show that it performs better than the most advanced algorithms currently available [65]. [PERSON] et al. proposed creating advanced emotional recognition characteristics by utilizing convolutional neural networks and long short-term memory networks. Two 1D CNN LSTM networks and one 2D CNN LSTM network were created to acquire both local and global emotion-related characteristics from speech and log-Mel spectrograms. Both networks share a comparable structure, including four local feature learning blocks (LFLBs) and one long short-term memory layer. The networks that were built, which integrate the advantages of both networks, were assessed using two standard datasets. The experiments demonstrated that the presented networks, particularly the 2D CNN LSTM network, outperformed conventional methods, such as DBN and CNN. The 2D CNN LSTM network acquired recognition accuracies of 95.33% and 95.89% on the Berlin EmoDB and 89.16% and 52.14% on IEMOCAP, respectively. In comparison, the accuracy attained by DBN and CNN was 73.78% and 40.02% [66]. [PERSON] et al. suggested a novel speech emotion detection system created by employing a convolutional neural network and utilizing a dedicated dataset together with a state-of-the-art graphics processing unit for the purposes of training, classification, and testing. The model attains a remarkable accuracy of 83.61%, surpassing comparable tasks on the dataset, showcasing the efficacy of CNN in speech recognition [67]. [PERSON] and [PERSON] suggested employing a cascaded denoising convolutional neural network (DnCNN)-CNN structure for the purpose of categorizing emotions in Korean and German languages under noisy circumstances. The architecture comprises two stages: the initial stage employs residual learning to remove noise, while the subsequent step carries out categorization. The DnCNN-CNN surpasses the baseline CNN in terms of overall accuracy for each of the languages. In the context of the Korean language, the DnCNN-CNN model achieves a precision rate of 95.8%, whereas the CNN model has a little lower precision rate of 93.6%. The DnCNN-CNN model achieves an average accuracy of 59.3-76.6% for the German language, whereas the CNN model achieves an average accuracy of 39.4-58.1%. The discoveries provide fresh perspectives on the understanding of emotions in speech under challenging circumstances and have ramifications for the development of speech emotion detection systems that are applicable across different languages [68]. [PERSON] et al. propose a classification and prediction model that employs multiple techniques, such as linear regression, random forest, decision trees, support vector machines, and convolutional neural networks. The paradigm categorizes human emotions into the following categories: neutral, calm, happy, surprised, sad, afraid, and disgusted. The RAVDEES dataset was used to evaluate the model, and the CNN achieved an accuracy of 78.20% in emotion recognition [69]. [PERSON] et al. created a system that combines three classifiers: a recurrent neural network, a convolution neural network, and a deep neural network. The paradigm was employed for the categorical identification of four distinct emotions: anger, happiness, neutrality, and sadness. The frame-level low-level descriptors (LLDs), segment-level Mel-spectrograms (MSs), and utterance-level outputs of high-level statistical functions (HSFs) were individually inputted into the DNN, RNN, and CNN models. They obtained three distinct models: MS-CNN, LLD-RNN, and HSF-DNN. The CNN and RNN outputs were amalgamated through the utilization of the attention mechanism-based weighted-pooling method. The categorization of discrete categories and the regression of continuous characteristics were both carried out simultaneously as part of a multi-task learning method that was used in order to acquire generalized features.