<![CDATA[Recognition of the speaker's emotions is an important but challenging component of Human-Computer Interaction (HCI). The need for the recognition of the speaker's emotions is also increasing related to the need for digitizing the company's operational processes related to the implementation of industry 4.0. The use of Deep Learning methods is currently increasing, especially for processing unstructured data such as data from voice signals. This study tries to apply the Deep Learning method to classify the speaker's emotions using an open dataset from SAVEE which contains seven classes of voice emotions in English. The dataset will be trained using the CNN model. The final accuracy of the model is 88% on the training data and 52% on the test data, which means the model is overfitting. This is due to the imbalance of emotion classes in the dataset, which makes the model tend to predict classes with more labels. In addition, the lack of heterogeneity of the dataset makes the character of the emotion class more different from the others so that it can reduce the bias in the model so as not to overfit the model. Further development of this research can be done, such as over-sampling the existing dataset by adding other data sources, then performing data augmentation to get the data character of each emotion class and setting hyperparameter values to get better accuracy values.]]>
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