Stefan Steidl
Papers
14
Total Citations
827
H-Index
12
About
Stefan Steidl is a leading researcher in affective computing and speech processing, whose work has fundamentally advanced how machines understand human emotion—particularly in spontaneous, real-world interactions. His research centers on automatic emotion recognition from speech, with a major focus on acoustic and linguistic feature analysis, child-robot interaction, and the development of realistic emotional speech corpora. Steidl’s most impactful contribution is the creation of the FAU Aibo Emotion Corpus, a landmark database capturing children’s spontaneous emotional speech while interacting with Sony’s pet robot AIBO (cited over 140 times). This corpus has become a standard benchmark in the field. His seminal 2007 paper on feature types for emotional classification (189 citations) systematically compared low-level descriptors and functionals, setting a methodological foundation for the community. Steidl has also explored the role of prosody, intimacy in speech, and the integration of automatic speech recognition (ASR) into emotion detection pipelines. His work bridges engineering and psychology, demonstrating that private emotional states differ markedly from socially displayed ones. With over 700 total citations, Steidl’s research continues to shape the design of emotionally aware, child-friendly interactive systems.
Research Focus
Key Achievements
Top Papers
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- 5Emotion recognition from speech: Putting ASR in the loop52 citations · 2009
- 6Patterns, prototypes, performance: classifying emotional user states41 citations · 2008
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- 8The prosody of pet robot directed speech: evidence from children35 citations · 2006
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