# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Text-Based Emotion Recognition
The pooling scores are inputted into an LSTM layer and subsequently into a DNN with a softmax activation function. The results indicate a lack of proficiency in accurately identifying the cheerful emotion category [38]. [PERSON] and [PERSON] introduced a resilient depiction of a tweet. Two parallel architectures were devised to build the representation utilizing different pretrained embeddings. The initial architecture derived the embedding matrix by combining emoji2 vec, GloVe, and character-level embeddings. The resulting matrix was inputted into a bidirectional long short-term memory model. Subsequently, the result of each time step was inputted into an attention layer. The second architecture utilized pretrained GloVe embeddings, which were trained on a Twitter corpus, to construct the embedding matrix. The matrix was inputted into a separate Bi-LSTM, and the output of the Bi-LSTM was subjected to max-pooling. The results of the two architectures were combined and subsequently inputted into two completely connected networks. Their model earned second place among the teams in the SemEval-2018 competition [39]. [PERSON] and his colleagues introduced a deep learning algorithm designed to identify and classify emotions in written conversations. The three segments of the discussion are combined and fed into the embedding layer. The output of the embedding layer is sent through three consecutive layers of Bi-LSTM, which are trained using average stochastic gradient descent. Subsequently, a self-attention mechanism is employed on the initial and last segments of the talk, followed by an average pooling operation. The disparity between the two combined scores is used as an input to a two-deep neural network, which is then followed by a softmax function to produce the emotion labels. The language model was trained using the Wikitext-103 dataset. The results indicate a lack of proficiency in identifying the cheerful emotion category [35]. A deep learning model was presented by [PERSON] and colleagues for the purpose of detecting multi-label emotions in microblogs. During the preprocessing stage, they utilized the ekphrasis tool. The word embedding procedure known as GloVe was utilized. The embedding layer is inputted into two Bi-GRU layers. After that, the first attention layer receives input from the embedding layer as well as the output of the first Bi-GRU layer. Additionally, the embedding layer, the output of the first Bi-GRU layer, and the output of the second Bi-GRU layer are all inputs that are supplied into the second attention layer. After that, the two attention layers are joined together and fed into a deep neural network that has a sigmoid activation function in order to carry out the classification. They attained cutting-edge outcomes with their model [40]. [PERSON] et al. defined emotion recognition in text as a problem of classifying text into two categories. Two word embedding models, namely, ConceptNet Numberbatch and fastText, were utilized. The embedding layer is inputted into a bidirectional gated recurrent unit layer. Subsequently, a combination of global max-pooling and average pooling layers is employed. The pooling scores are inputted into a deep neural network, and a sigmoid layer is used to carry out the classification. The findings indicate that deep learning models have the ability to acquire more meaningful characteristics, leading to a significant enhancement in performance [41]. [PERSON] et al. introduced a sophisticated deep learning model designed specifically for the purpose of recognizing emotions in multimedia text. The word2 vec model was employed to construct the word embeddings. The embedding layer is inputted into the convolutional layers, subsequently followed by a max-pooling layer and a DNN layer. The output of the deep neural network is subsequently inputted into an attention layer. The categorization is executed using the softmax function.