## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Literature** & **Acoustic Features** & **Classifier** & \begin{tabular}{c} **Highest** \\ **Accuracy** \\ \end{tabular} & **Database** & \begin{tabular}{c} **Number of** \\ **Emotions** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{**Discrete Emotions**} \\ \hline [PERSON] et al. (2017) [PERSON] et al. (2017) (2017) & \begin{tabular}{c} Correlation, cepstral \\ distance, MFCC, \\ prosodic \\ \end{tabular} & SVM & \textless{}80\% & EMO-DB & 3,4,6 \\ \hline [PERSON] et al. (2018) [PERSON] et al. (2018) (2018) & \begin{tabular}{c} LPCCVQC \\ MFCCVQC \\ PLPVQC \\ \end{tabular} & \begin{tabular}{c} MLP \\ RBFN \\ PNN, DNN \\ \end{tabular} & \begin{tabular}{c} 83\% \\ 79\% \\ 76\% \\ \end{tabular} & EMO-DB & 4 \\ \hline Proposed & \begin{tabular}{c} Baseline + timbre \\ selected \\ \end{tabular} & \begin{tabular}{c} SVM, \\ LSTM-RNN \\ \end{tabular} & \begin{tabular}{c} 96.49\% \\ \end{tabular} & EMO-DB & 4 \\ \hline \multicolumn{6}{c}{**Binary Valence**} \\ \hline [PERSON] et al. (2016) [PERSON] et al. (2016) & \begin{tabular}{c} eGeMAPS \\ ComParE \\ \end{tabular} & SVM & \begin{tabular}{c} 78.1\% \\ 86.7\% \\ \end{tabular} & EMO-DB & 2 \\ \hline proposed & \begin{tabular}{c} Baseline + timbre \\ selected \\ \end{tabular} & \
\begin{tabular}{c} SVM, \\ LSTM-RNN \\ \end{tabular} &
\begin{tabular}{c} 97.87\% \\ \end{tabular} & EMO-DB & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results in literature with the proposed acoustic feature set for EMO-DB. Figure 6: Recognition rates of positive and negative emotions on baseline, timbre all, timbre selected, and the combination of baseline and timbre selected acoustic feature sets for IEMOCAP. During the experiments that applied the SFS method, it was found that some of the spectral shapes (spectral skewness, kurtosis, slope, decrease, roll-off, flatness) and harmonic (harmonic energy, noise energy, harmonic spectral deviation, and odd-to-even harmonic ratio) features of timbre were all unable to characterize the emotions from speech as well as the features in the timbre that had been selected for the EMO-DB. In addition, the timbre selected consisted of only nine acoustic features, which was very useful in terms of dimensionality compared to a large-scale brute-force acoustic feature set for building an automatic SER system. Furthermore, it can characterize emotions in the valence dimension. For the EMO-DB, overall, the SVM and the LSTM-RNN gave good results for the combination of the baseline and timbre selected acoustic feature sets. The timbre features also improved the recognition rates of emotions for the IEMOCAP. The improvement was not as high as for EMO-DB. The IEMOCAP database was originally designed to research emotion recognition from multiple modalities, including facial expressions, gesture, and speech. We only used speech to recognize the emotions, and this might not have been enough to achieve higher results for this database. However, the results are comparable to the literature results. We achieved higher results using the LSTM-RNN in terms of classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Acoustic Features** & **Classifier** & **Highest Accuracy** & **Database** & **Number of Emotions** \\ \hline \multicolumn{5}{c}{Discrete emotions} \\ \hline [PERSON] et al. (2015) [99] & LLDs & BLSTM-ELM & 63.89\% & IEMOCAP & 4 \\ [PERSON] et al. (2017) [39] & LLDs & BLSTM-WPA & 58.8\% & IEMOCAP & 4 \\ [PERSON] et al. (2017) [45] & Spectrogram & LSTM & 58.05\% & IEMOCAP & 4 \\ [PERSON] et al. (2017) [38] & Statistical & LSTM & 60.02\% & IEMOCAP & 4 \\ Proposed & Timbre all & LSTM-RNN & 65.06\% & IEMOCAP & 4 \\ \hline \multicolumn{5}{c}{Bidirectional LSTM with extreme learning machine (BLSTM-ELM) and weighted pooling attention (BLSTM-WPA).} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the results in the literature with the proposed acoustic feature set for IEMOCAP.