## 5 Results and Discussion
Figure 5 shows the recognition rates of discrete emotions obtained using SVM and LSTM-RNN for different acoustic feature sets. It is clear from Figure 5 that the recognition rates for all emotions were significantly better for the combination of the baseline and timbre selected acoustic feature sets compared to the recognition rates for the rest of the acoustic feature sets. The recognition rates for angry and happy emotions increased substantially for the timbre all acoustic feature set, but the changes in the results for neutral and sad emotions were not significant. Timbre features improved the discrimination of emotions that have the same level of pitch and loudness. Baseline acoustic features can discriminate emotions when the acoustic features of emotions in speech, such as pitch and loudness, are different. In Table 6, comparison of the results in the literature with a proposed feature set was given for EMO-DB. From Table 6, it can be seen that the proposed feature set increased recognition accuracy for both the discrete emotions classification and the binary valence classification. Figure 4: 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 EMO-DB. Figure 5: Recognition rates for discrete emotions on baseline, timbre all, timbre selected, and the combination of baseline and timbre selected acoustic features for EMO-DB. The results on IEMOCAP were different from EMO-DB. As can be seen from Table 5, the highest recognition rates for binary valence classification (74%) and the classification of discrete emotions (65.06%) were achieved using the timbre all acoustic feature set. The results obtained using the timbre selected acoustic feature set were higher than the baseline feature set and close to the timbre all feature set. Although the recognition rates on a combination of the baseline and the timbre selected feature set were better than the baseline feature set, they were lower than the results on the timbre all feature set. The baseline and timbre selected feature sets complemented each other on the IEMOCAP database. Figure 6 shows the recognition rates of positive and negative emotions achieved using the baseline, timbre all, timbre selected, and the combination of baseline and timbre selected feature sets for binary valence classification. The highest recognition rate for positive emotion was obtained using the timbre all feature set increased the recognition rate of positive emotion, but it decreased for the timbre selected feature set when using SVM. The difference between recognition rates of negative emotion in all feature sets was not significant. Figure 7 presents the recognition rates of anger, happiness, neutrality, and sadness obtained using all feature sets. It is clear from Figure 7 that the timbre all feature set improved the recognition rate of all emotions except anger compared to the baseline feature set. In both classifiers, the recognition rate of emotions decreased with the timbre selected feature set compared to the timbre all feature set. Table 7 shows the comparison of the results in literature with the proposed acoustic feature set for IEMOCAP. It is clear from Table 7 that the accuracy obtained using the timbre all feature set is higher than the other results in the literature. Acoustic features used in the literature is given in Table 8.