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

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 elearning. 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.