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Convolutional brain emotional learning (CBEL) model

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Abstract

In this article, the new cognitive model convolutional brain emotional learning (CBEL) is introduced to recognize emotional speech on the Berlin dataset. This model is an improved model of brain emotional learning (BEL), which is inspired by the limbic function of the brain. The reason for choosing this model is that the limbic system of the brain is responsible for emotion, so it can help to recognize the emotion of speech. In the proposed method the input signals will be sent to the convolutional neural network (CNN) for the feature extraction. Then, changes have been made in the CNN pooling layer to increase the processing speed. Finally, the output of the pooling layers was sent to the Multi-Layer Perceptron (MLP) networks in the amygdala and orbitofrontal, from the CBEL model, to display the recognition rate on each basic emotional state. The results of the experiments show that the accuracy of emotional speech recognition of the proposed model is 97.39% and it has performed better than other methods introduced in the article.

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Data availability

There are many standard data, such as RAVDESS, eNTERFACE’ 05, etc., that can be used for this purpose.

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Authors and Affiliations

  1. Department of Computer, Fouman & Shaft Unit, Islamic Azad University, Fouman, Iran

    Sara Motamed & Elham Askari

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  1. Sara Motamed
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  2. Elham Askari
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Correspondence to Sara Motamed.

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Motamed, S., Askari, E. Convolutional brain emotional learning (CBEL) model. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01819-9

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