Mustaqeem Khan
Papers
1
Total Citations
24
H-Index
1
About
Mustaqeem Khan is a rising researcher in affective computing and speech signal processing, with a focus on efficient, real-world emotion recognition. His work centers on developing lightweight, high-performance models that bridge the gap between academic accuracy and practical deployment. His most cited paper, "TC-Net: A Modest & Lightweight Emotion Recognition System Using Temporal Convolution Network" (2023, 24 citations), introduces a compact temporal convolutional architecture for Speech Emotion Recognition (SER). This contribution is notable for achieving competitive accuracy while significantly reducing model complexity, making it suitable for resource-constrained environments like mobile health and call center analytics. By prioritizing efficiency without sacrificing performance, Khan’s research addresses a critical bottleneck in deploying SER systems in real-world applications. His work has already garnered attention for its practical impact, offering a scalable solution for human-computer interaction in healthcare and beyond. As an emerging voice in the field, Khan is shaping the next generation of accessible, deployable emotion-aware technologies.
Research Focus
Key Achievements
Top Papers
- 1