Umniah Hameed Jaid

About

Umniah Hameed Jaid is a researcher advancing the field of automatic speaker profiling (ASP), which focuses on estimating physical traits such as gender, age, ethnicity, and height directly from voice recordings. Her work bridges signal processing and deep learning to address critical challenges in forensics, surveillance, customer service, and human-robot interaction. In her highly cited 2023 review, Jaid systematically surveyed ASP features, methods, and challenges—providing a foundational resource that has already garnered 5 citations. She further contributed a novel end-to-end approach using 1D convolutional neural networks with filter bank initialization, achieving efficient and accurate speaker profiling for real-time applications (4 citations). Jaid’s research stands out for its practical focus on deploying ASP in latency-sensitive environments, such as mobile shopping and robotics, while maintaining robustness across diverse demographic groups. Her work not only synthesizes the state of the art but also pushes toward deployable, lightweight models—making her a notable voice in the growing intersection of speech technology and artificial intelligence.

Research Focus

Key Achievements

Top Papers

1. 1
   Review of Automatic Speaker Profiling: Features, Methods, and Challenges

   5 citations · 2023
2. 2
   End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization

   4 citations · 2023

Key Collaborators

Contact & Links