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Social Signal Processing: Survey of an Emerging Domain
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 153 (32 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.
Social Signal Processing: State-of-the-art and future perspectives of an emerging domain
- IN PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 27 (7 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.
Audiovisual Laughter Detection Based on Temporal Features ABSTRACT
"... Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audio-visual approach to distinguishing laughter from speech based on temporal features and we show that integrating the information from audio and video channels leads to ..."
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Cited by 22 (8 self)
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Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audio-visual approach to distinguishing laughter from speech based on temporal features and we show that integrating the information from audio and video channels leads to improved performance over single- modal approaches. Static features are extracted on an audio/video frame basis and then combined with temporal features extracted over a temporal window, describing the evolution of static features over time. The use of several different temporal features has been investigated and it has been shown that the addition of temporal information results in an improved performance over utilizing static information only. It is common to use a fixed set of temporal features which implies that all static features will exhibit the same behaviour over a temporal window. However, this does not always hold and we show that when AdaBoost is used as a feature selector, different temporal features for each static feature are selected, i.e., the temporal evolution of each static feature is described by different statistical measures. When tested on 96 audiovisual sequences, depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, in a person independent way the proposed audiovisual approach achieves an F1 rate of over 89%.
Audiovisual discrimination between laughter and speech
- In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing
, 2008
"... Past research on automatic laughter detection has focused mainly on audio-based detection. Here we present an audiovisual approach to distinguishing laughter from speech and we show that integrating the information from audio and video leads to an improved reliability of audiovisual approach in comp ..."
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Cited by 17 (9 self)
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Past research on automatic laughter detection has focused mainly on audio-based detection. Here we present an audiovisual approach to distinguishing laughter from speech and we show that integrating the information from audio and video leads to an improved reliability of audiovisual approach in comparison to single-modal approaches. We also investigated the level at which audiovisual information should be fused for the best performance. When tested on 96 audiovisual sequences depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, the proposed audiovisual feature-level approach achieved a 86.9 % recall rate with 76.7 % precision. Index Terms — Audiovisual data processing, laughter detection, data fusion, nonlinguistic information processing.
Fusion of audio and visual cues for laughter detection
- In ACM Intern. Conf. on Image and Video Retrieval
, 2008
"... Past research on automatic laughter detection has focused mainly on audio-based detection. Here we present an audiovisual approach to distinguishing laughter from speech and we show that integrating the information from audio and video channels leads to improved performance over singlemodal approach ..."
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Cited by 13 (6 self)
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Past research on automatic laughter detection has focused mainly on audio-based detection. Here we present an audiovisual approach to distinguishing laughter from speech and we show that integrating the information from audio and video channels leads to improved performance over singlemodal approaches. Each channel consists of 2 streams (cues), facial expressions and head movements for video and spectral and prosodic features for audio. We used decision level fusion to integrate the information from the two channels and experimented using the SUM rule and a neural network as the integration functions. The results indicate that even a simple linear function such as the SUM rule achieves very good performance in audiovisual fusion. We also experimented with different combinations of cues with the most informative being the facial expressions and the spectral features. The best combination of cues is the integration of facial expressions, spectral and prosodic features when a neural network is used as the fusion method. When tested on 96 audiovisual sequences, depicting spontaneously displayed (as opposed to posed) laughter and speech episodes, in a person independent way the proposed audiovisual approach achieves over 90 % recall rate and over 80 % precision.
Decision-level fusion for audio-visual laughter detection
- in: MLMI ’08: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
, 2008
"... Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed b ..."
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Cited by 12 (6 self)
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Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA-PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio. We compared hidden Markov models (HMMs), Gaussian mixture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better performance than single-modality classification. 3 4
Audiovisual discrimination between speech and laughter: Why and when visual information might help
- IEEE Trans. on Multimedia
, 2011
"... Abstract—Past research on automatic laughter classification/detection has focused mainly on audio-based approaches. Here we present an audiovisual approach to distinguishing laughter from speech, and we show that integrating the information from audio and video channels may lead to improved performa ..."
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Cited by 9 (5 self)
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Abstract—Past research on automatic laughter classification/detection has focused mainly on audio-based approaches. Here we present an audiovisual approach to distinguishing laughter from speech, and we show that integrating the information from audio and video channels may lead to improved performance over single-modal approaches. Both audio and visual channels consist of two streams (cues), facial expressions and head pose for video and cepstral and prosodic features for audio. Two types of experiments were performed: 1) subject-independent cross-validation on the AMI dataset and 2) cross-database experiments on the AMI and SAL datasets. We experimented with different combinations of cues with the most informative being the combination of facial expressions, cepstral, and prosodic features. Our results suggest that the performance of the audiovisual approach is better on average than single-modal approaches. The addition of visual information produces better results when it comes to female subjects. When the training conditions are less diverse in terms of head movements than the testing conditions (training on the SAL dataset, testing on the AMI dataset), then no improvement was observed with the addition of visual information. On the other hand, when the training conditions are similar (cross validation on the AMI dataset), or more diverse (training on the AMI dataset, testing on the SAL dataset), in terms of head movements than is the case in the testing conditions, an absolute increase of about 3% in the F1 rate for laughter is reported when visual information is added to audio information. Index Terms—Human behavior analysis, laughter-versusspeech discrimination, neural networks, principal components analysis (PCA). I.
AUDIOVISUAL CLASSIFICATION OF VOCAL OUTBURSTS IN HUMAN CONVERSATION USING LONG-SHORT-TERM MEMORY NETWORKS
"... We investigate classification of non-linguistic vocalisations with a novel audiovisual approach and Long Short-Term Memory (LSTM) Recurrent Neural Networks as highly successful dynamic sequence classifiers. As database of evaluation serves this year’s Paralinguistic Challenge’s Audiovisual Interest ..."
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Cited by 7 (3 self)
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We investigate classification of non-linguistic vocalisations with a novel audiovisual approach and Long Short-Term Memory (LSTM) Recurrent Neural Networks as highly successful dynamic sequence classifiers. As database of evaluation serves this year’s Paralinguistic Challenge’s Audiovisual Interest Corpus of human-to-human natural conversation. For video-based analysis we compare shape and appearance based features. These are fused in an early manner with typical audio descriptors. The results show significant improvements of LSTM networks over a static approach based on Support Vector Machines. More important, we can show a significant gain in performance when fusing audio and visual shape features.
AUDIOVISUAL VOCAL OUTBURST CLASSIFICATION IN NOISY ACOUSTIC CONDITIONS
"... In this study, we investigate an audiovisual approach for classification of vocal outbursts (non-linguistic vocalisations) in noisy conditions using Long Short-Term Memory (LSTM) Recurrent Neural Networks and Support Vector Machines. Fusion of geometric shape features and acoustic low-level descript ..."
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Cited by 1 (1 self)
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In this study, we investigate an audiovisual approach for classification of vocal outbursts (non-linguistic vocalisations) in noisy conditions using Long Short-Term Memory (LSTM) Recurrent Neural Networks and Support Vector Machines. Fusion of geometric shape features and acoustic low-level descriptors is performed on the feature level. Three different types of acoustic noise are considered: babble, office and street noise. Experiments are conducted on every noise type to asses the benefit of the fusion in each case. As database for evaluations serves the INTERSPEECH 2010 Paralinguistic Challenge’s Audiovisual Interest Corpus of human-to-human natural conversation. The results show that even when training is performed on noise corrupted audio which matches the test conditions the addition of visual features is still beneficial.
