Alif Bin Abdul Qayyum

Bangladesh University of Engineering and Technology

Papers

Total Citations

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About

Alif Bin Abdul Qayyum is a researcher whose work sits at the intersection of artificial intelligence and human-computer interaction, with a primary focus on speech-emotion recognition. His most cited paper, "Convolutional Neural Network (CNN) Based Speech-Emotion Recognition" (2019, 69 citations), represents a significant contribution to affective computing by demonstrating how deep learning architectures can effectively decode emotional states from vocal patterns. This work has practical implications for cybersecurity, mental health monitoring, and human-robot interaction, as it enables machines to interpret the emotional nuances embedded in natural speech. By leveraging CNNs to analyze acoustic features, Qayyum has helped advance the field's ability to detect emotions like anger, sadness, and joy with improved accuracy. His research addresses the growing need for emotionally intelligent systems that can understand not just what people say, but how they say it. With applications ranging from preventing cyber crimes to enhancing virtual assistants, Qayyum's work continues to influence how AI systems perceive and respond to human affect, making him a notable voice in the evolving landscape of speech-based emotion recognition.