# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 2 Literature Review and Related Works  
From a psychological point of view, Refs. [15; 16], human emotions are categorized into six major groups, which fall under positive and negative emotion, respectively. However, this can also be sub-divided into valence or arousal. Valence emotion determines the extent to which emotion is either positive or negative, while arousal indicates the level of intensity. The description of these basic emotions are shown in Table 1. These emotions goes through the process of vectorization in speech data processing (encoding) before emotional features extraction and classification can be performed. For more than half a decade, several deep transfer learning techniques rooted in deep convolutional neural network (DCNN) have been applied in speech recognition with the utmost focus on emotion classification from speech. Transfer learning minimized computational cost while maintaining an accurate extraction of features from speech signals [17; 18]. Despite several outstanding records of successful application of convolutional neural network (CNN), the SEC domain is still facing the intense challenge of optimal performance with minimal complexity. As the literature is saturated with many of the applications of deep transfer learning techniques for an improved performance in speech emotion classification, this work has reviewed some published works. Obviously, the recent advancement in Artificial Intelligence and affective computing has conspicuously made the deep transfer learning model very popular in major fields, such as image segmentation, visual objects recognition systems, facial emotion recognition \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotion** & **Description** & **Expression** & **Class** \\ \hline Happiness & \begin{tabular}{c} A state of pleasantness \\ characterized by joyful mood \\ \end{tabular} & Upbeat & Positive \\ \hline Sadness & \begin{tabular}{c} A transitional emotion state \\ usually characterized by \\ grieving felling \\ \end{tabular} & Leathery and dampened & Negative \\ \hline Fear & \begin{tabular}{c} Plays a vital role in survival \\ \end{tabular} & Rapid heartbeat & Positive/Negative \\ \hline Disgust & \begin{tabular}{c} Results from unpleasant taste \\ or smell \\ \end{tabular} & Retching/nauseating & Negative \\ \hline Anger & \
\begin{tabular}{c} A state of hot temper, hostility \\ and aggression \\ \end{tabular} & Yelling & Negative \\ \hline Surprise &
\begin{tabular}{c} Usually occurs when the \\ unexpected happens \\ \end{tabular} & Screaming and hilarious & Positive/negative \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic Emotion Description. systems, age and gender estimation, and speech recognition systems, which is due to its extraordinary performance in prediction and low error rate. [PERSON] et al. [19] applied a pretrained DCNN-based transfer learning for speech emotion classification. A multi-user speech dataset was used in training their model, while fine tuning took place on the speaker-dependent speech emotion dataset. The first 11 layers of the model that served in feature learning were frozen from being trained according to the structural setup of the model with utmost the aim of fine-tuning the model on the target's dataset input. [PERSON] and [PERSON], Ref. [20] investigated the possibility of adopting a double stream approach of CNN and DCNN for extracting spectral and spatial features for an improved performance in SEC. Aside from the fusion of dual features techniques, considerable effort was made by applying a feature selection algorithm for the effective classification of emotion. [PERSON] and [PERSON]. [21] proposed an attention-based convolutional neural network with a fully connected layer trained in an end-to-end manner for the effective classification of speech emotion. An optimized DNN was proposed by [PERSON] and [PERSON], Ref. [22] for real-time speech emotion classification. The optimization was carried out using stochastic gradient descent.