G. Chaithanya
Papers
1
Total Citations
3
H-Index
1
About
G. Chaithanya is a researcher at the forefront of affective computing and speech emotion recognition, with a focus on leveraging machine learning to decode human emotional states from vocal data. Their most-cited work, "Comparative Analysis of Machine Learning Models for Emotion Classification in Speech Data" (2024, 3 citations), provides a rigorous benchmark by evaluating models across diverse, widely-used datasets—RAVDESS, SAVEE, CREMA, and TESS—covering a full spectrum of emotions from neutral to fear. This study not only advances the field of human-computer interaction but also offers practical insights for applications in psychology and medicine. By systematically comparing model performance, Chaithanya’s work helps researchers and practitioners select optimal approaches for real-world emotion detection. Their contributions are particularly notable for bridging the gap between algorithmic development and the nuanced, multimodal nature of emotional expression, making their research a valuable resource for students and professionals exploring speech-based affective systems.
Research Focus
Key Achievements
Top Papers
- 1