Mercedes Torres Torres
Papers
2
Total Citations
15
H-Index
2
About
Dr. Mercedes Torres Torres is a leading researcher at the intersection of affective computing, privacy-preserving machine learning, and speech technology. Her work addresses critical challenges in human-computer interaction, particularly how to build systems that understand human emotion without compromising individual privacy. In her highly cited 2022 paper, "Facial identity protection using deep learning technologies: an application in affective computing" (8 citations), she pioneered methods to decouple facial identity from emotional expression, enabling accurate valence and arousal prediction while safeguarding personal data—a foundational contribution to ethical AI. More recently, her 2024 overview, "An overview of high-resource automatic speech recognition methods and their empirical evaluation in low-resource environments" (7 citations), provides a vital roadmap for adapting deep learning ASR systems to data-scarce settings, bridging a critical gap in accessibility. Dr. Torres Torres’s work has direct implications for robotics, mental health monitoring, and inclusive technology. Her dual focus on privacy and resource efficiency positions her as a key voice in responsible AI development, with her research already shaping how next-generation affective and speech systems are designed for real-world, ethical deployment.
Research Focus
Key Achievements
Top Papers
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