Ayesha Pervaiz

University of Engineering and Technology Taxila

Papers

Total Citations

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About

Ayesha Pervaiz is a leading researcher in speech processing and machine learning, with a primary focus on improving the robustness of automatic speech recognition (ASR) systems in real-world, noisy environments. Her most cited work, "Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data" (2020, 55 citations), makes a pivotal contribution to the field by demonstrating how strategically augmenting training datasets with diverse noise profiles can dramatically enhance a model's ability to understand speech commands in challenging acoustic conditions. This research addresses a critical bottleneck in deploying voice-controlled devices—from smart assistants to industrial tools—by moving beyond clean, laboratory-trained models toward systems that perform reliably in everyday settings. Pervaiz’s work is notable for its practical, data-centric approach, offering a scalable solution that leverages existing corpora without requiring complex architectural changes. By tackling the fundamental problem of noise interference, her research has significant implications for human-computer interaction, accessibility, and the next generation of voice-enabled technologies, establishing her as a key voice in advancing robust, real-world speech recognition.