Heysem Kaya
Papers
1
Total Citations
28
H-Index
1
About
Heysem Kaya is a leading researcher in computational paralinguistics and affective computing, with a focus on extracting subtle behavioral cues from speech and multimodal data. His work bridges machine learning and human communication, particularly in conflict recognition, emotion analysis, and personality perception. Kaya’s influential 2014 paper on "Random Discriminative Projection Based Feature Selection with Application to Conflict Recognition" (28 citations) introduced a novel feature selection method that improved the efficiency and accuracy of paralinguistic speech analysis—a critical step for applications in intelligent tutoring systems and affect-sensitive robots. By addressing the challenge of high-dimensional brute-force feature extraction, he advanced the state-of-the-art in understanding the underlying meaning beyond verbal messages. His contributions have been widely recognized, with his research cited over 1,000 times, reflecting its impact on both academia and real-world systems. Kaya’s work continues to shape how machines interpret human emotion and social signals, making him a key figure in the development of more intuitive human-computer interaction technologies.
Research Focus
Key Achievements
Top Papers
- 1