Papers
12
Total Citations
115
H-Index
5
About
Anja Austermann is a human-robot interaction researcher whose work bridges affective computing, natural language processing, and social robotics. Her research focuses on two interconnected themes: enabling robots to recognize and express emotions through speech, and understanding how humans naturally communicate with robotic systems of varying appearances and capabilities. Austermann's most influential contributions center on emotion recognition for the robot head MEXI, where she pioneered fuzzy logic approaches to analyzing prosodic features in natural speech, allowing the robot to both detect emotional states and produce emotionally expressive output. These foundational papers have garnered over 50 citations combined, establishing her early expertise in affective human-robot dialogue. She further advanced the field by developing multimodal reward-learning systems, combining Hidden Markov Models with classical conditioning principles to help robots interpret human feedback delivered through speech, gesture, and touch. Her comparative studies examining how users interact with humanoid robots like ASIMO versus pet-like robots such as AIBO shed valuable light on how robot morphology influences human communication strategies during teaching tasks. This body of work, accumulating over 100 citations across her portfolio, provides meaningful guidance for designers of social robots intended to learn from natural human interaction, making Austermann a notable contributor to the foundations of intuitive human-robot communication.
Research Focus
Key Achievements
Top Papers
- 1Fuzzy emotion recognition in natural speech dialogue30 citations · 2006
- 2Prosody based emotion recognition for MEXI22 citations · 2005
- 3How do users interact with a pet-robot and a humanoid19 citations · 2010
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