What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- The Internet
Artificial intelligence, Human–computer interaction, Multimedia, Presentation and Cognitive psychology are her primary areas of study.
Her work deals with themes such as Speech recognition, Computer vision and Natural language processing, which intersect with Artificial intelligence.
Her Speech recognition research incorporates elements of Affective computing and Feature.
Her Human–computer interaction study combines topics from a wide range of disciplines, such as Interface and Cultural computing.
Her biological study spans a wide range of topics, including Realization, Task, Computer graphics, Presentation logic and World Wide Web.
She interconnects Facial expression, Embodied agent, Deception and Affect in the investigation of issues within Cognitive psychology.
Her most cited work include:
- Emotion recognition based on physiological changes in music listening (659 citations)
- The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (528 citations)
- From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification (376 citations)
What are the main themes of her work throughout her whole career to date?
The scientist’s investigation covers issues in Human–computer interaction, Multimedia, Artificial intelligence, Context and User interface.
Elisabeth André studied Human–computer interaction and Embodied cognition that intersect with Conversation.
The concepts of her Multimedia study are interwoven with issues in World Wide Web, Presentation, Interactive storytelling and Style.
Many of her studies on Presentation apply to Graphics as well.
Her research in Artificial intelligence intersects with topics in Machine learning, Speech recognition, Computer vision and Natural language processing.
Her research is interdisciplinary, bridging the disciplines of Feature extraction and Speech recognition.
She most often published in these fields:
- Human–computer interaction (38.72%)
- Multimedia (25.69%)
- Artificial intelligence (17.06%)
What were the highlights of her more recent work (between 2016-2021)?
- Human–computer interaction (38.72%)
- Artificial intelligence (17.06%)
- Machine learning (4.04%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Human–computer interaction, Artificial intelligence, Machine learning, Robot and Cognitive psychology.
Her Human–computer interaction study which covers Context that intersects with Personality.
Her work on Pattern recognition expands to the thematically related Artificial intelligence.
Elisabeth André regularly ties together related areas like Annotation in her Machine learning studies.
Her study in the fields of Human–robot interaction under the domain of Robot overlaps with other disciplines such as Social robot.
Many of her studies involve connections with topics such as Interface and User experience design.
Between 2016 and 2021, her most popular works were:
- Adapting a Robot's linguistic style based on socially-aware reinforcement learning (33 citations)
- How to Shape the Humor of a Robot - Social Behavior Adaptation Based on Reinforcement Learning (29 citations)
- Multi-modal Pain Intensity Recognition based on the SenseEmotion Database (23 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Human–computer interaction
- Programming language
Her main research concerns Human–computer interaction, Artificial intelligence, Robot, Reinforcement learning and Social robot.
Her Human–computer interaction research includes themes of Task and Embodied cognition.
Her work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
The Robot study combines topics in areas such as Sonic interaction design, Personality, Nonverbal communication and Adaptation.
Her studies deal with areas such as Context and Laughter as well as Adaptation.
Her Reinforcement learning research is multidisciplinary, incorporating perspectives in Intelligent agent, Contextual image classification and Salience.
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