Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Robust Real-Time Face Detection
International Journal of Computer Vision
Smile and Laughter Recognition using Speech Processing and Face Recognition from Conversation Video
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Automatic discrimination between laughter and speech
Speech Communication
A Novel Feature for Emotion Recognition in Voice Based Applications
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Real-Time Emotion Recognition from Speech Using Echo State Networks
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Contrasting emotion-bearing laughter types in multiparticipant vocal activity detection for meetings
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Is this joke really funny? judging the mirth by audiovisual laughter analysis
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Support vector machine for functional data classification
Neurocomputing
On the use of nonverbal speech sounds in human communication
COST 2102'07 Proceedings of the 2007 COST action 2102 international conference on Verbal and nonverbal communication behaviours
Robust real time face tracking for the analysis of human behaviour
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
RASTA-PLP speech analysis technique
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
How low level observations can help to reveal the user's state in HCI
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
How low level observations can help to reveal the user's state in HCI
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Perception markup language: towards a standardized representation of perceived nonverbal behaviors
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
On instance selection in audio based emotion recognition
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
A companion technology for cognitive technical systems
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
Classification of social laughter in natural conversational speech
Computer Speech and Language
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It is essential for the advancement of human-centered multimodal interfaces to be able to infer the current user's state or communication state. In order to enable a system to do that, the recognition and interpretation of multimodal social signals (i.e., paralinguistic and nonverbal behavior) in real-time applications is required. Since we believe that laughs are one of the most important and widely understood social nonverbal signals indicating affect and discourse quality, we focus in this work on the detection of laughter in natural multiparty discourses. The conversations are recorded in a natural environment without any specific constraint on the discourses using unobtrusive recording devices. This setup ensures natural and unbiased behavior, which is one of the main foci of this work. To compare results of methods, namely Gaussian Mixture Model (GMM) supervectors as input to a Support Vector Machine (SVM), so-called Echo State Networks (ESN), and a Hidden Markov Model (HMM) approach, are utilized in online and offline detection experiments. The SVM approach proves very accurate in the offline classification task, but is outperformed by the ESN and HMM approach in the online detection (F1 scores: GMM SVM 0.45, ESN 0.63, HMM 0.72). Further, we were able to utilize the proposed HMM approach in a cross-corpus experiment without any retraining with respectable generalization capability (F1score: 0.49). The results and possible reasons for these outcomes are shown and discussed in the article. The proposed methods may be directly utilized in practical tasks such as the labeling or the online detection of laughter in conversational data and affect-aware applications.