## 5 Results and Discussion  
One of the primary challenges in pattern recognition is finding the best features that can be correctly discriminated with recognition. In speech recognition, as well as in emotion recognition from speech, MFCC and energy are known to be the most useful acoustic features. Moreover, GeMAPS can be considered a standard acoustic feature set in music classification, speech recognition, and the SER [5]. However, some emotions are difficult to discriminate using MFCC and energy, such as anger and happiness. In most previous studies [48; 57; 49], classification accuracy of the happy emotion was low. The best accuracy rate (86.7%) was achieved using a large-scale brute-force acoustic feature set (6373 acoustic features), and 78.1% accuracy was achieved using extended GeMAPS for binary valence classification on EMO-DB [5]. Although the highest accuracy rate was achieved using a large-scale brute-force acoustic feature set, it is too difficult in terms of extraction time to use those features for real-time speech emotion recognition systems. The numbers of extracted features, along with the extraction time, are also crucial factors to consider when training pattern models and building a real-time speech emotion recognition system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acoustic Feature Set** & **Classifiers** & **Binary Valence** & **Discrete Emotions** \\ \hline \multirow{2}{*}{Baseline} & SVM & 69.16\% & 74.72\% \\  & LSTM-RNN & **73.81\%** & **77.72\%** \\ \hline \multirow{2}{*}{Timbre all} & SVM & **84.75\%** & 85.67\% \\  & LSTM-RNN & 81.76\% & **86.58\%** \\ \hline \multirow{2}{*}{Timbre selected} & SVM & **85.58\%** & **86.07\%** \\  & LSTM-RNN & 80.33\% & 84.24\% \\ \hline \multirow{2}{*}{Baseline + timbre selected} & SVM & **97.87\%** & 96.2\% \\  & LSTM-RNN & 97.46\% & **96.49\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average accuracy rate for all experiments for EMO-DB. (The bold numbers indicate the highest accuracy rate). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acoustic Feature Set** & **Classifiers** & **Binary Valence** & **Discrete Emotions** \\ \hline \multirow{2}{*}{Baseline} & SVM & 71\% & 57.3\% \\  & LSTM-RNN & **72\%** & **58.58\%** \\ \hline \multirow{2}{*}{Timbre all} & SVM & 71\% & 60.39\% \\  & LSTM-RNN & **74\%** & **65.06\%** \\ \hline \multirow{2}{*}{Timbre selected} & SVM & 68\% & 57.85\% \\  & LSTM-RNN & **72\%** & **62.07\%** \\ \hline \multirow{2}{*}{Baseline + timbre selected} & SVM & 72\% & 59.75\% \\  & LSTM-RNN & **73\%** & **63.03\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy rate in all experiments for IEMOCAP. (The bold indicates the highest accuracy rate). First, we examined the results for the EMO-DB. In our experiment, 97.87%, which was the highest accuracy rate (Table 4), was obtained using the combination of baseline and timbre selected acoustic features for binary valence classification. The combination of the baseline and timbre selected acoustic features sets also gave the highest accuracy rate for discrete emotions classification. The difference in accuracy rate between the timbre all and timbre selected acoustic feature sets was less than 2%. However, the number of acoustic features in timbre selected was almost two times less than the number of features in the timbre all feature set. This means that irrelevant features in timbre were all removed when the SFS was applied. The results were improved for the combination of the baseline and timbre selected acoustic feature sets compared to the rest of the acoustic feature sets. Consequently, it was clear that baseline and timbre features complemented each other. Figure 4 shows the recognition rates of positive and negative emotions obtained using all feature sets for binary classification. The recognition rates of both positive and negative emotions improved for the combination of the baseline and timbre selected feature set compared to the rest of feature sets on both SVM and LSTM-RNN.