V. Sushvitha
Papers
1
Total Citations
3
H-Index
1
About
V. Sushvitha is a rising researcher in affective computing and speech processing, with a focused interest in the intersection of machine learning and human emotion recognition. Her most cited work, "Comparative Analysis of Machine Learning Models for Emotion Classification in Speech Data" (2024), systematically evaluates multiple algorithms across four benchmark datasets—RAVDESS, SAVEE, CREMA, and TESS—covering a broad emotional spectrum including neutral, surprise, happiness, sadness, disgust, anger, and fear. This study provides critical insights into model performance for real-world applications in psychology, medicine, and human-computer interaction. With 3 citations already in a short time, her work is gaining traction for its rigorous comparative methodology and practical relevance. Sushvitha’s contributions help bridge the gap between raw acoustic features and reliable emotion detection, offering a foundation for more empathetic and responsive AI systems. Her research is particularly valuable for students and practitioners seeking to understand which models best capture the nuances of human emotional expression in speech, making her a promising voice in this rapidly evolving field.
Research Focus
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Top Papers
- 1