# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Voice-Based Emotion Recognition
examined the application of parallel convolutional neural network networks for the purpose of automatic speech emotion recognition tasks. The researchers utilized the Ryerson Audio-Visual Dataset of Speech and Song (RAVDESS) for training the networks. The researchers integrated a convolutional neural network with attention-based networks to effectively capture and represent both spatial and temporal characteristics. In order to enhance the training data, techniques such as SpecAugment, additive white Gaussian noise, Tanh distortion, and room impulse response were implemented. CNN's image classification capabilities were utilized to convert the raw audio data into Mel-spectrograms. In order to represent temporal feature representations, Transformer and BLSTM-Attention modules were implemented. The proposed architectures were evaluated against standalone CNN architectures, attention-based networks, and hybrid architectures. The Parallel CNN-Transformer network had a maximum accuracy of 89.33%, while the Parallel CNN-BLSTM-Attention Network achieved an accuracy of 85.67%. These results are promising, especially considering that the parallel models have far fewer training parameters compared to non-parallel hybrid models [77]. [PERSON] et al. describe the creation of EmoRec, an iOS app specifically built to forecast emotions based on audio input. The platform provides a user friendly interface for users to capture and submit audio data, which can be utilized for future emotion recognition research. The application utilizes deep learning algorithms that are most effective when applied to extensive datasets, hence enhancing the outcomes. The article provides a comprehensive account of the entire development process, encompassing design, optimization, and testing. It also delves into the outcomes of the process and outlines potential future endeavors [78]. The popularity of automatic speech emotion detection is increasing because of its capacity to identify emotions in speech. Nevertheless, speaking frequently includes moments of quiet that may not be pertinent to the expression of emotion. In order to enhance performance, [PERSON] and [PERSON] suggested implementing a mix of silence removal and attention models. The findings indicate that this strategy surpasses the performance of solely employing noise removal or attention models in the task of speech emotion recognition [79]. [PERSON] et al. suggested a novel data augmentation algorithm that enhances the speech emotions dataset by incorporating noise fractions. According to the authors, the hyperparameters of contemporary deep learning models are either manually crafted or altered during the training process; however, this does not ensure that the optimal settings are achieved. The authors present an enhanced deep learning model that optimizes these parameters, leading to improved recognition outcomes. The model comprises a convolutional neural network that incorporates local feature-learning blocks and a long short-term memory layer. This architecture enables the model to learn both local and long-term correlations in the log-Mel-spectrogram of input speech samples. Using the stochastic fractal search (SFS)-guided whale optimization algorithm, the learning rate and label smoothing regularization factor are optimized. The approach achieves the most optimal global solution by balancing the exploration and exploitation of search agents' positions. The proposed method is evaluated using four speech emotion datasets, reaching recognition accuracies of 98.13%, 99.76%, 99.47%, and 99.50% for each dataset, respectively. A statistical examination of the results highlighted the robustness of the proposed methodology [80]. [PERSON] et al. investigated the application of long short-term memory models in the field of cross-corpus acoustic emotion recognition. The approach utilizes a method that predicts the valence and arousal of each frame to classify the impact of each speech.