Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Bi-modal emotion recognition from expressive face and body gestures
Journal of Network and Computer Applications
Learning AAM fitting through simulation
Pattern Recognition
GMM-SVM Kernel with a Bhattacharyya-based distance for speaker recognition
IEEE Transactions on Audio, Speech, and Language Processing
Opensmile: the munich versatile and fast open-source audio feature extractor
Proceedings of the international conference on Multimedia
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Affective Computing
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
Automatic speaker age and gender recognition using acoustic and prosodic level information fusion
Computer Speech and Language
Proceedings of the 14th ACM international conference on Multimodal interaction
Affective Body Expression Perception and Recognition: A Survey
IEEE Transactions on Affective Computing
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Quantifying behavioural changes in depression using affective computing techniques is the first step in developing an objective diagnostic aid, with clinical utility, for clinical depression. As part of the AVEC 2013 Challenge, we present a multimodal approach for the Depression Sub-Challenge using a GMM-UBM system with three different kernels for the audio subsystem and Space Time Interest Points in a Bag-of-Words approach for the vision subsystem. These are then fused at the feature level to form the combined AV system. Key results include the strong performance of acoustic audio features and the bag-of-words visual features in predicting an individual's level of depression using regression. Interestingly, in the context of the small amount of literature on the subject, is that our feature level multimodal fusion technique is able to outperform both the audio and visual challenge baselines.