Biswarup Ray
Papers
1
Total Citations
17
H-Index
1
About
Biswarup Ray is a researcher in speech processing and machine learning, with a focus on feature selection and neural network optimization for voice-based identification tasks. His most cited work, "GRaNN: feature selection with golden ratio-aided neural network for emotion, gender and speaker identification from voice signals" (2022, 17 citations), introduces a novel approach that leverages the golden ratio to enhance neural network performance in extracting discriminative features from complex voice signals. This contribution addresses the inherent challenges of speaker variability, including language, accent, and emotional state, by improving accuracy in emotion, gender, and speaker recognition. Ray's research bridges signal processing and artificial intelligence, offering practical solutions for biometrics and human-computer interaction. His work has garnered attention for its innovative use of mathematical principles in feature selection, demonstrating significant impact in the field of voice analysis. With a growing citation record, Ray continues to advance the frontiers of intelligent voice recognition systems.
Research Focus
Key Achievements
Top Papers
- 1