Shohei Nakagawa

Papers

Total Citations

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About

Shohei Nakagawa is a researcher whose work lies at the intersection of affective computing and human-robot interaction, with a primary focus on emotion recognition from speech signals. His most cited paper, "Emotion recognition method based on normalization of prosodic features" (2013, 5 citations), addresses a fundamental challenge in creating natural conversational robots: the variability of prosodic features across different speakers and contexts. Nakagawa’s key contribution is the development of a normalization technique that mitigates these variations, enabling more robust and accurate emotion classification from speech. This work is critical for advancing technologies where machines must interpret human emotional states to respond appropriately. While his citation count is modest, the foundational nature of his research has implications for broader fields such as affective computing, assistive robotics, and human-computer interaction. Nakagawa’s approach highlights the importance of preprocessing and feature standardization in speech emotion recognition, offering a practical solution that can be integrated into larger systems. His work represents a stepping stone toward more empathetic and responsive artificial agents, making him a notable contributor to the ongoing effort to bridge the gap between human emotional expression and machine understanding.