# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 2 Related Studies  
Speech emotion recognition research has been exhilarating for a long time and several papers have presented different ways of developing systems for recognizing human emotions. The authors in [3] presented a majority voting technique (MVT) for detecting speech emotion using fast correlation -based feature (FCBF) and Fisher score algorithms for feature selection. They extracted 16 low-level features and tested their methods over several machine learning algorithms, including artificial neural network (ANN), classification and regression tree (CART), support vector machine (SVM) and K-nearest neighbor (KNN) on berlin emotion speech database (Emo-DB). [PERSON] et al. (2017) proposed a speech emotion recognition method that extracted MFCC with modulation spectral (MS) features and used recurrent neural network (RNN) learning algorithm to classify seven emotions from Emo-DB and Spanish database. The authors in ([PERSON] et al., 2017) proposed a speech emotion recognition model where glottis was used for compensation of glottal features. They extracted features of glottal compensation to zero crossings with maximal teager (GCZCMT) energy operator using Taiyuan University of technology (TYUT) speech database and Emo-DB. Evaluation of feature selection algorithms on a combination of linear predictive coefficient (LPC), MFCC and prosodic features with three different multiclass learning algorithms to detect speech emotion was discussed in ([PERSON] et al., 2017). [PERSON] et al. (2017) used 324 spectral and 54 prosody features combined with five voice quality features to test their proposed speech emotion recognition method on the Surrey audio-visual expressed emotion (SAVEE) database after applying the minimal redundancy maximal relevance (mRMR) to reduce less discriminating features. In ([PERSON] et al., 2018), a method for recognizing emotions in an audio conversation based on speech and text was proposed and tested on the SemEval-2007 database using SVM. [PERSON] et al. (2018) used the extreme learning machine (ELM) method for feature selection that was applied to 938 features based on a combination of spectral and prosodic features from Emo-DB for speech emotion recognition. The authors in ([PERSON] et al., 2018) extracted 988 spectral and prosodic features from three different databases using the OpenSmile toolkit with SVM for speech emotion recognition. [PERSON] et al. (2018) introduced the generalized discriminant analysis (GerDA) based on DNN to recognize emotions from speech using the OpenEar specialized software to extract 6552 acoustic features based on 39 functional of 56 acoustic low-level descriptor (LLD) from Emo-DB and speech under simulated and actual stress (SUSAS) database. [PERSON] et al. (2018) applied the cooperative learning method to recognize speech emotion from FAU Aibo and SUSAS databases using GCZCMT based acoustic features. In ([PERSON] et al., 2018), Hu moments based weighted spectral features (HuWSF) were extracted from Emo-DB, SAVEE and Chinese academy of sciences - institute of automation (CASIA) databases to classify emotions from speech using SVM. The authors used HuWSF and multicluster feature selection (MCFS) algorithm to reduce feature dimensionality. In ([PERSON] et al., 2018), extended Geneva minimalistic acoustic parameter set (eGeMAPS) features were used to classify speech emotion, gender and age from the Ryerson audio-visual database of emotional speech and song (RAVDESS) using the multiple layer perceptron (MLP) neural network. [PERSON] et al. (2018) analyzed 6920 acoustic features from the interactive emotion dyadic motion capture (IEMOCAP) database to discover that features based on groups of MFCC, LPC and cochleagrams are important for estimating valence, activation and dominance emotions in speech respectively. The authors in ([PERSON] et al., 2018) proposed a DNN architecture for extracting informative feature representatives from heterogeneous acoustic feature groups that may contain redundant and unrelated information.