Advancements in accurate speech emotion recognition through the integration of CNN-AM model

Marion Olubunmi Adebiyi, Timothy T. Adeliyi, Deborah Olaniyan, Julius Olaniyan


In this study, we introduce an innovative approach that combines convolutional neural networks (CNN) with an attention mechanism (AM) to achieve precise emotion detection from speech data within the context of e-learning. Our primary objective is to leverage the strengths of deep learning through CNN and harness the focus-enhancing abilities of attention mechanisms. This fusion enables our model to pinpoint crucial features within the speech signal, significantly enhancing emotion classification performance. Our experimental results validate the efficacy of our approach, with the model achieving an impressive 90% accuracy rate in emotion recognition. In conclusion, our research introduces a cutting-edge method for emotion detection by synergizing CNN and an AM, with the potential to revolutionize various sectors.


attention mechanism; convolution neural network; emotion; recognition; signal; speech;

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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