On the Necessity and Feasibility of Detecting a Driver's Emotional State While Driving

Authors:
Michael Grimm;Kristian Kroschel;Helen Harris;Clifford Nass;Björn Schuller;Gerhard Rigoll;Tobias Moosmayr
Affiliations:
Universität Karlsruhe (TH), Institut für Nachrichtentechnik, 76128 Karlsruhe, Germany;Universität Karlsruhe (TH), Institut für Nachrichtentechnik, 76128 Karlsruhe, Germany;Stanford University, Department of Communication, Stanford, CA 94305-2050, USA;Stanford University, Department of Communication, Stanford, CA 94305-2050, USA;Technische Universität München, Institute for Human-Machine Communication, 80290 München, Germany;Technische Universität München, Institute for Human-Machine Communication, 80290 München, Germany;BMW Group, Forschungs- und Innovationszentrum, Akustik, Komfort und Werterhaltung, 80788 München, Germany
Venue:
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Year:
2007

Citing 6
Cited 13

The nature of statistical learning theory

The nature of statistical learning theory
Speech interfaces from an evolutionary perspective

Communications of the ACM
Improving automotive safety by pairing driver emotion and car voice emotion

CHI '05 Extended Abstracts on Human Factors in Computing Systems
Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
Automatic recognition of affective cues in the speech of car drivers to allow appropriate responses

OZCHI '05 Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future
Primitives-based evaluation and estimation of emotions in speech

Speech Communication

Automatic Recognition of Emotions from Speech: A Review of the Literature and Recommendations for Practical Realisation

Affect and Emotion in Human-Computer Interaction
Switching Linear Dynamic Models for Noise Robust In-Car Speech Recognition

Proceedings of the 30th DAGM symposium on Pattern Recognition
Recognition of noisy speech: a comparative survey of robust model architecture and feature enhancement

EURASIP Journal on Audio, Speech, and Music Processing
Exploration of Affect Sensing from Speech and Metaphorical Text

Edutainment '09 Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development
Whodunnit - Searching for the most important feature types signalling emotion-related user states in speech

Computer Speech and Language
Emotion on the road: necessity, acceptance, and feasibility of affective computing in the car

Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction
Affective speaker state analysis in the presence of reverberation

International Journal of Speech Technology
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Speech Communication
Using emotional classification model for travel information system

International Journal of Computational Science and Engineering
A systematic approach to using music for mitigating affective effects on driving performance and safety

Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Robust emotional speech classification in the presence of babble noise

International Journal of Speech Technology
Experiential perspectives on road congestions

CHI '13 Extended Abstracts on Human Factors in Computing Systems
Society of Mind cognitive agent architecture applied to drivers adapting in a traffic context

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper brings together two important aspects of the human-machine interaction in cars: the psychological aspect and the engineering aspect. The psychologically motivated part of this study addresses questions such as whyit is important to automatically assess the driver's affective state, which states are important and how a machine's response should look like. The engineering part studies howthe emotional state of a driver can be estimated by extracting acoustic features from the speech signal and mapping them to an emotion state in a multidimensional, continuous-valued emotion space. Such a feasibility study is performed in an experiment in which spontaneous, authentic emotional utterances are superimposed by car noise of several car types and various road surfaces.