Alia Karim Abdulhassan

About

Alia Karim Abdulhassan is a researcher specializing in speaker profiling and deep learning for audio signal processing. Her work focuses on the automatic estimation of speaker characteristics—including height, age, and gender—using advanced neural network architectures. Her most-cited paper, "End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization" (2023), introduces a novel approach that leverages one-dimensional convolutional neural networks with filter bank initialization to achieve rapid and accurate speaker profiling. This innovation addresses critical needs in forensics, surveillance, customer service, and human-robot interaction, where real-time responses are essential. With 4 citations, her work is gaining recognition for its practical implications in real-world applications. Abdulhassan’s contributions advance the field of speaker characterization, offering efficient solutions that bridge the gap between deep learning research and deployable technology. Her research continues to impact areas requiring swift, reliable identification of speaker traits, marking her as an emerging voice in audio-based AI systems.

Research Focus

Key Achievements

Top Papers

1. 1
   End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization

   4 citations · 2023

Key Collaborators

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