## 2 Literature Review  
Numerous works has been done for emotion detection from the speech signals using both machine and deep learning techniques. However, to a great extent, performance and efficacy of the proposed model depends on dataset used for model training. This section is divided into two sections; first analyses few pre-constructed speech datasets for SER along with their shortcomings and second reviews some latest research contributions for the development of effective SER classification models using DL techniques. An Urdu database for speech emotion recognition have been introduced for the training of ML models. The data collection comprises of recorded audios for seven emotions with 734 Urdu and 701 Sindhi utterances [24]. Moreover, the authors employed baseline classification using OpenSmile feature extraction toolkit in terms of UAR. The results show that the regression models applied on ComParE performs better classification, achieving 56.03% accuracy for Urdu and Sindhi SER. The collection of Urdu-Sindhi Corpus encompasses only 10 scripted sentences for every type of emotion. The major limitation of such recordings is that, for instance, a specific scripted sentence utterance may represent more than one emotion. Moreover, in such cases speaker is not representing real world situation. In another acted Urdu SER database, ref. [25] collected utterances of 25 words from 50 speakers (25 male and 25 female) based on three emotions. Nonetheless, the employment of such small-scale Urdu databases using DL models would be inappropriate and may not be useful for the development of real time SER applications. Ref. [26] formulated first custom URDU dataset for Urdu language. In URDU dataset 400 audio records were collected from different Urdu television talk shows representing 4 basic emotions. Though, the collected data contained unscripted spontaneous conversations of anchors with their guests, representation of actual emotions in TV talk shows is quite unusual because the most dominant emotion in such conversations is neutral or aggressive. Since the training of deep learning models require huge amount of data, the small URDU dataset may lead to over fitting or under fitting problems. In a study, ref. [27] used an endways multi-learning trick (MLT) based on 1D enhanced CNN model for automatic extraction of local and global emotional features from acoustic signals. For the enhancement of recognition rate, the proposed solution extracted the discriminative features using dynamic fusion framework. The proposed multi-learningmodel evaluated both short as well as long-term relative dependencies over two benchmark SER databases, IEMOCAP and EMO-DB, with 73% and 90% accuracy rates, respectively. However, the method relatively takes more time to train and test the real time speech signals as compared to other models. Exploring two datasets, RECOLA (to employ regression) and IEMOCAP (for classification task), ref. [28] detected the emotions in speech using a novel end-to-end DNN algorithm. The authors found that SER performance in simulation results was optimum when proposed method applied with RMS aggregation and context stacking. The proposed DiCCOSER-CS model improved the arousal CCC by 9.5% and the valence CCC by 12.7% in regression task as compared with CNN-LSTM. Recurrent Neural Networks models have also been implemented with slight variations for feature learning in SER systems. A similar study [29] illustrates deep RNN, learning frame-level categorization as well as temporal collection into long time intervals. Also, the authors presented weighted time-pooling scheme to classify emotionally prominent fragments from the speech signals using simple attention mechanism. The model achieved 5.7% weighted and 3.1% unweighted classification accuracy using IEMOCAP dataset, when compared with conventional SVM-SER model. Although, the model introduced novel technique for emotion recognition, the experiments were conducted using only one database.