Papers
16
Total Citations
871
H-Index
13
About
Anton Batliner is a leading figure in the field of affective computing and speech processing, with a career dedicated to understanding how emotion is encoded in human speech, particularly in human-robot interaction. His research focuses on the automatic classification of emotional user states, acoustic and linguistic feature analysis, and the development of realistic, spontaneous speech corpora. Batliner’s major contributions include pioneering work on the automatic recognition of emotion from children’s speech during interactions with pet robots, most notably the AIBO robot. His highly cited 2007 paper on feature type relevance (189 citations) established critical methodologies for using low-level descriptors and functionals in emotion classification. His 2004 cross-linguistic AIBO corpus (140 citations) remains a foundational resource in the field. Batliner’s work has also extended to clinical applications, as seen in his 2017 study on classifying autistic child vocalisations. With over 700 total citations across his top papers, his research has profoundly shaped how machines interpret emotional prosody and social signals, bridging the gap between acoustic analysis and real-world affective interaction.
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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- 9The prosody of pet robot directed speech: evidence from children35 citations · 2006
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