Fundamentals of speech recognition
Fundamentals of speech recognition
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
A unifying review of linear Gaussian models
Neural Computation
Learning nonlinear dynamical systems using an EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning for Dynamic State Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Particle filtering with factorized likelihoods for tracking facial features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Affective Computing
The development and real-world application of FROG, the fun robotic outdoor guide
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Fusing multiple continuous expert annotations is a crucial problem in machine learning and computer vision, particularly when dealing with uncertain and subjective tasks related to affective behaviour. Inspired by the concept of inferring shared and individual latent spaces in probabilistic CCA (PCCA), we firstly propose a novel, generative model which discovers temporal dependencies on the shared/individual spaces (DPCCA). In order to accommodate for temporal lags which are prominent amongst continuous annotations, we further introduce a latent warping process. We show that the resulting model (DPCTW) (i) can be used as a unifying framework for solving the problems of temporal alignment and fusion of multiple annotations in time, and (ii) that by incorporating dynamics, modelling annotation/sequence specific biases, noise estimation and time warping, DPCTW outperforms state-of-the-art methods for both the aggregation of multiple, yet imperfect expert annotations as well as the alignment of affective behavior.