Pengenalan Emosi Pembicara Menggunakan Convolutional Neural
Networks

Pengenalan Emosi Pembicara Menggunakan Convolutional Neural
Networks

Convolutional Neural Networks, Deep Learning, Keras, Speech Emotion Recognition, Tensorflow

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

Rendi Nurcahyo1
, Mohammad Iqbal2

, Universitas Gunadarma

: 27-02-2022

Fajar bagus W

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Indonesia

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