# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 3 Materials and Methods  
Materials used for this study included speech emotion multimodal databases of RAVDESS [51] and SAVEE [52]. The databases, because of their popularity were chosen to test for the effectiveness \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Database** & \
\begin{tabular}{c} **Type of** \\ **Recording** \\ \end{tabular} & **Number of Emotions** & **Feature Set** &
\begin{tabular}{c} **Classification** \\ **Method** \\ \end{tabular} & **Result (\%)** \\ \hline
[3] & Emo-DB & Acted & \begin{tabular}{c} Energy + MHCC + \\ 2 CR + \(voicing\) \\ \end{tabular} & \
\begin{tabular}{c} \\ 2 CR + \(voicing\) \\ probability + \(+\) \\ \end{tabular} &
\begin{tabular}{c} \\ 84.19 \\ \end{tabular} \\ \hline
[11] & Spanish & Acted & \
\begin{tabular}{c} Anger, Disgust, Fear, \\ Neutral, Surprise, \\ Sadness, Joy \\ \end{tabular} & MFCC + MS &
\begin{tabular}{c} RNN \\ classifier \\ \end{tabular} & 90.05 \\ \hline
[15] & Emo-DB & Acted &
\begin{tabular}{c} Anger, Happy, \\ Neutral \\ \end{tabular} & GCZCMT & SVM & 84.45 \\ \hline
[19] & Emo-DB & Acted & \begin{tabular}{c} Anger, Happiness, \\ Neutral, Sadness \\ \end{tabular} & \
\begin{tabular}{c} Prosodic + \\ sub-band + MFCC \\ + LPC \\ \end{tabular} &
\begin{tabular}{c} SFS \\ algorithm + \\ SVM \\ \end{tabular} & 83.00 \\ \hline
[28] & eNTERFACE’05 & Elicited & \
\begin{tabular}{c} Disgust, Surprise, \\ Happy, Anger, Sad, \\ Fear, \\ \end{tabular} &
\begin{tabular}{c} Pitch, energy, \\ formants, intensity \\ and ZCR + text \\ \end{tabular} & SVM & 90.00 \\ \hline
[29] & CASIA & Acted & \begin{tabular}{c} Neutral, Happy, \\ Sadness, Fear, Angry, \\ Surprise \\ \end{tabular} & \
\begin{tabular}{c} Prosodic + quality \\ characteristics + \\ MFCC \\ \end{tabular} &
\begin{tabular}{c} correlation \\ analysis + \\ Fisher + \\ ELM \\ \end{tabular} & 89.60 \\ \hline
[36] & IEMOCAP & Acted & \
\begin{tabular}{c} Anger, Happy, \\ Neutral, Sad \\ \end{tabular} &
\begin{tabular}{c} IS10 + MFCCs + \\ eGemaps + \\ SoundNet + \\ VGGish \\ \end{tabular} & SVM & 64.00 \\ \hline
[39] & Emo-DB & Acted & \
\begin{tabular}{c} Anger, Boredom, \\ Happy, Neutral, \\ Sadness \\ \end{tabular} &
\begin{tabular}{c} Prosodic features \\ + paralinguistic \\ features \\ \end{tabular} & SVM & 94.90 \\ \hline
[42] & CASIA & Acted &
\begin{tabular}{c} Surprise, Happy, Sad, \\ Angry, Fear, Neutral \\ \end{tabular} & MFCC & GA-BEL + \\
[46] & CASIA & Acted & \
\begin{tabular}{c} Anger, Fear, Happy, \\ Neutral, Surprise, Sad \\ \end{tabular} &
\begin{tabular}{c} MFCC, pitch, \\ format, ZCR and \\ short-term energy \\ \end{tabular} & SVM + DBN & 95.80 \\ \hline
[47] & Emo-DB & Acted & \
\begin{tabular}{c} Sadness, Fear, Joy, \\ Anger, Surprise, \\ Disgust, Neutral \\ \end{tabular} & eGeMAPS &
\begin{tabular}{c} CSS stacking \\ system \\ +SVM \\ \end{tabular} & 82.45 \\ \hline
[48] & Emo-DB & Acted & \
\begin{tabular}{c} Anger, Happiness, \\ Sadness, Boredom, \\ Neutral, Disgust, Fear, \\ \end{tabular} & MFCCs &
\begin{tabular}{c} bagged \\ ensemble of \\ SVMs \\ \end{tabular} & 92.45 \\ \hline
[49] & RAVDESS & Acted & \
\begin{tabular}{c} Neutral, Surprise, \\ Happy, Angry, Calm, \\ Sad, Fearful, Disgust \\ \end{tabular} &
\begin{tabular}{c} CWT, prosodic \\ coefficients \\ \end{tabular} & SVM & 60.10 \\ \hline
[50] & Spanish & Acted & \begin{tabular}{c} Anger, Joy, Disgust, \\ Neutral, Surprise, \\ Fear, Sadness \\ \end{tabular} & \
\begin{tabular}{c} SMFC, ECC, \\ MF, MS and \\ EFCC \\ \end{tabular} & RNN & 91.16 \\ \hline Proposed model & RAVDESS/SAVEE & Acted &
\begin{tabular}{c} Anger, Sad, Happy, \\ Disgust, Calm, Fear, \\ Neutral, Surprise \\ \end{tabular} & Prosodic + & RDF \\ spectral & ensemble & 99.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of our method with related methods. of the proposed HAF against two other sets of features. The study method followed three phases of feature extraction, feature selection and classification, which were subsequently described.