Lara Toledo Cordeiro Ottoni

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About

Lara Toledo Cordeiro Ottoni is a researcher at the forefront of affective computing and human-robot interaction (HRI). Her work centers on bridging the gap between human emotional expression and machine understanding, with a particular focus on speech emotion recognition (SER) and the integration of emotions into robotic systems. Ottoni’s most impactful contribution is her 2023 paper, "A Deep Learning Approach for Speech Emotion Recognition Optimization Using Meta-Learning," which has garnered 24 citations. This work introduces a novel meta-learning framework to optimize deep learning models for SER, significantly improving their ability to adapt to diverse emotional speech patterns—a critical step toward more natural and responsive human-machine interfaces. Beyond this, Ottoni has systematically explored the role of emotions in HRI. Her 2021 review, "A Review of Emotions in Human-Robot Interaction" (9 citations), and her 2024 systematic review (8 citations) provide comprehensive analyses of how robots can both recognize and express emotions to foster empathic interactions. These reviews synthesize existing methodologies and performance evaluations, offering a roadmap for future research. By advancing SER optimization and mapping the emotional landscape of HRI, Ottoni is helping to create robots that are not just intelligent, but emotionally intelligent—paving the way for applications in healthcare, entertainment, and assistive robotics.