## 4 Experiments
### Acoustic Feature Extraction and Selection  
First, we extracted the most popular and most used acoustic features in the SER field, which include the MFCC, Energy, and their first-order derivatives, using open-source media interpretation by large feature-space extraction (openSMILE) [51]. Several studies have used MFCC acoustic features for speech emotion recognition [52; 53; 54]. We also considered those acoustic features as a baseline to evaluate our acoustic features. To extract energy and its first-order derivative, a speech signal was divided into short 30-ms frames with 10 ms overlapping. Then, the energy and its first-order derivative were extracted from each frame. The MFCC and its first-order derivative were extracted as shown in Figure 3.  
We divided the acoustic features into three groups. The first group included MFCC, the energy and their first-order derivatives. We named this group the baseline acoustic features. The second group consisted of timbre features. The timbre features were extracted using the timbre toolbox [30] with MATLAB. In the timbre toolbox, both global (i.e., derived from the whole utterance) and local (i.e., extracted from frames) acoustic features can be extracted. Moreover, acoustic features can be extracted from different input representations (e.g., audio signal, short-term Fourier transform (STFT) amplitude and power, temporal energy envelope, and harmonic). We extracted local acoustic features from different input representations and experimented with them to find the most suitable input representation for our problem. The STFT (amplitude) and harmonic input representations were found to be the most suitable in our case through experiments. Classification accuracy was the criterion for choosing the most suitable input representations. Detailed information regarding extracting timbre features can be found in [30]. Our extracted timbre features consisted of 11 spectral features, which were derived from the STFT (amplitude), and 8 harmonic (the number of harmonics was set to 12) acoustic features. After extracting the timbre features, we also performed SFS [55] to find the best feature subset among all timbre acoustic features for the third group. In the SFS algorithm, the measure metric must be specified. It can be a particular error rate or classification accuracy. We chose classification accuracy as a measured metric. The working principle of this algorithm was as follows. In the first step, each acoustic feature is individually measured and the best one is chosen based on classification accuracy. A chosen feature remains in the second step, and then other features are added one by one. The best feature combination is selected in this step. After that, those chosen features are retained, and the others are added again one by one in the next step. The process goes on like this until the best feature subset is found. After performing the SFS, we found five spectral and four harmonic features for timbre. Finally, we had three groups of acoustic features. We named them baseline, timbre all, and timbre selected. The names of the acoustic features and their dimensions are given in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Emotions**} & **EMO-DB** & **IEMOCAP** \\ \cline{2-3}  & **Number of Samples** & **Number of Samples** \\ \hline Anger & 78 & 705 \\ Happiness & 69 & 705 \\ Neutral & 78 & 705 \\ Sadness & 60 & 705 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information about the databases, emotion categories, and the number of samples in each emotion category.