# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Voice-Based Emotion Recognition  
The speech emotion recognition system has two sequential steps: (1) Determine the appropriate characteristic of speech, and (2) determine a decision-making class that is based on the feelings that are stated. The vast majority of research conducted in the field of speech emotion recognition mainly concentrates on stage (2) due to its significance in bridging the gap between the challenges in this domain and the classification methods. Emotion identification systems have historically relied on traditional categorization. There are primarily two broad classifications of classifiers: standard classifiers and deep learning classifiers. Standard classifiers encompass classification algorithms, such as artificial neural networks (ANNs) [45], support vector machines (SVM) [46; 47], hidden Markov models (HMMs) [48; 49; 50; 51], Gaussian mixture models (GMMs) [52; 53; 54], K-nearest neighbors (K-NN) [46; 55], decision trees (DTs) [56; 57; 58], linear discriminant analysis (LDA) [56; 59; 60; 61], and the maximum likelihood method [62]. On the other hand, deep learning classifiers, such as convolution neural networks, deep neural networks, recurrent neural networks, deep belief networks (DBNs), long short-term memory, and deep Boltzmann machine (DBM), are used for speech emotion recognition. There is a lack of consensus over which classification is more suitable. Each type appears to possess distinct benefits and constraints. Deep neural networks are computational models inspired by the structure and functioning of the human brain. The task of speech emotion recognition has been shown to be difficult due to the variability of emotional states in various contexts. There exist multiple architectures in this domain, with the convolutional neural network being the most renowned and extensively employed. A convolutional neural network is a very proficient and transparent technique in the field of deep learning. [PERSON] et al. suggested that the CNN architecture be utilized in speech emotion recognition (SER) for the purpose of accurately identifying emotions in continuous speech. The database used for the model training is the Acted Emotional Speech Dynamic Database (AESDD). The CNN design surpasses earlier models in both accuracy and efficiency by a margin of 8.4%, eliminating the need for manual feature extraction. Data augmentation enhances the ability of a system to handle unexpected situations and apply knowledge to new scenarios. It also improves the system's ability to perform well on a wide range of tasks. The unsupervised feature extraction step enables real-time systems to be implemented effectively [63]. [PERSON] et al. proposed a technique for recognizing voice emotions using spectrograms and a deep convolutional neural network. The model is composed of three convolutional layers and three fully connected layers. It is designed to extract distinctive features from spectrogram images and generates predictions for seven different moods. The model underwent training using spectrograms from the Berlin emotions dataset and employed transfer learning by utilizing a pretrained AlexNet model. The initial findings indicate that the recently trained method surpasses the fine-tuned model in reliably and efficiently forecasting emotions [64]. [PERSON] et al. present a unique approach to voice recognition that combines frame-level speech data with attention-based long short-term memory recurrent neural networks. Waveforms are analyzed to extract speech features at the frame level, ensuring that the original timing relations of the speech are preserved. Two enhancement tactics are suggested for LSTM models by utilizing the attention mechanism: altering the forgetting gate of conventional LSTM while maintaining performance and implementing an attention mechanism to both the temporal and feature dimensions in the ultimate output.