Wisha Zehra

Papers

Total Citations

138

H-Index

About

Wisha Zehra is a leading researcher in affective computing and human-robot interaction, with a primary focus on cross-lingual speech emotion recognition. Her most-cited work, "Cross corpus multi-lingual speech emotion recognition using ensemble learning" (2021, 138 citations), addresses a critical challenge in the field: enabling robots to accurately interpret emotional cues from speakers across diverse languages and cultural backgrounds. By pioneering ensemble learning techniques that generalize across multiple speech corpora, Zehra has significantly advanced the robustness of emotion detection systems, moving beyond single-language, single-corpus models. Her research directly impacts the development of more empathetic and culturally aware service robots, improving human-robot communication in real-world, multilingual settings. This foundational paper has become a key reference for researchers tackling cross-corpus variability in affective computing. Zehra’s work continues to bridge the gap between machine learning and social robotics, making her a notable contributor to the next generation of emotionally intelligent autonomous systems.