# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 2 Literature Review and Related Works
Learning the most salient features from a speech spectrogram image through a light-weight CNN model for an improved performance in SEC was presented by [PERSON] et al. [23]. The model was also trained in an end-to-end mode, with an enhanced pooling strategy and fewer parameters. A deep transfer learning model that is based on AlexNet with correlation-based feature selection techniques was presented by [PERSON] et al. [24] in order to investigate the overall impact of the feature selection algorithm on speech emotion classification. [PERSON] et al. [25] evaluated three different feature selection techniques, namely, Relief feature Infinite Latent Feature Selection, and a generalized Fisher score for speech emotion recognition, along with the recently proposed automatic feature selection method. They employed three datasets from three different languages to affirm that higher accuracy can only be achieved through a reduced feature set. [PERSON] et al. [26] adopted transfer learning using a pretrained model on ImageNet (AlexNet) to extract segment-level features and an attention mechanism for learning emotionally rich features. Their model also combine Bi-directional Long Short-Term Memory (BiLSTM) for effective speech emotion classification using two different datasets (EMOCDB and IEMOCAP). [PERSON] and [PERSON]. [27] proposed a transfer learning technique with distance loss for speech emotion classification. They fine-tuned a siamese nueral network in order to achieve a state-of-the-art result. An experiment was carried out on the eNTERFACE, RAVDESS, and CREMA-D datasets, respectively. Spectrogram features generated from speech signals were augmented in [PERSON] et al. [28] and fed as input into a transfer learning model (ResNet) with a significant pooling layer (statistics) for speech emotion classification. They prevented overfitting and improved classification accuracy in spectrogram augmentation techniques. [PERSON] et al. [29] proposed a deep neural network BiLSTM for emotion classification using a cross dataset that was rooted in EEG (electroencephalography). Out of the three datasets (DEAP, SEED, IDEA) used in their work, two were for training, while one (IDEA) was for testing the performance of their proposed method. Four classifiers (SVM, KNN, MLP and Deep RNN) were trained with four speech features (Hjorth parameters, DE, LF-DE and PSD), and the results of their experiments indicated that DNN parameters optimization can positively enhance the performance of the emotion classification system. In addition, the deep BiLSTM model improved emotion recognition from EGG through the extraction of discriminative features and temporal dependencies of speech samples. However, the datasets utilized were insufficient for a typical Deep BiLSTM network model learning, which eventually hampered the accuracy (59%) recorded. The most popular datasets used within the speech emotion domain consist of synthetic data because of the difficulty and security issues associated with real-world speech-based datasets. However, [PERSON] and [PERSON] [30] carried out a speech emotion classification task using a real-world dataset (from phone calls). In their work, a deep learning network made up of two LSTM layers and fully connected layers with an Adam optimizer for back propagation was utilized. Their model was first tested on three popular datasets which included IEMOCAP, RAVDESS and TESS with accuracy of 60.4% and 86.02% before the real-world data obtained from a call center was used. The real dataset was based on Lavitian Language, and a model accuracy of 52.48% was recorded. It very evident from this work that the result is lower than state-of-the-art; moreover, the testing accuracy on the real-world dataset used was not stated. Really, it is very cumbersome to build a standardized speech corpus for emotion recognition, and this is a major difficulty confronting speech-based emotion classification tasks. Some of these deep learning methods adopted so far require extensive training, which in most cases increases computational cost.