Probabilistic Slow Features for Behavior Analysis

Probabilistic linear discriminant analysis with bottleneck features for speech recognition

We have recently proposed a new acoustic model based on probabilistic linear discriminant analysis (PLDA) which enjoys the flexibility of using higher dimensional acoustic features, and is more capable to capture the intra-frame feature correlations. In this paper, we investigate the use of bottleneck features obtained from a deep neural network (DNN) for the PLDA-based acoustic model. Experime...

Concensus of Self-features for Nonverbal Behavior Analysis

One of the key challenge in social behavior analysis is to automatically discover the subset of features relevant to a specific social signal (e.g., backchannel feedback). The way that these social signals are performed exhibit some variations among different people. In this paper, we present a feature selection approach which first looks at important behaviors for each individual, called self-...

Discovering Relational Domain Features for Probabilistic Planning

In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the feasible problem size. We consider the problem of automatically finding useful domain features in problem domains that exhibit relational structure. Specifically we consider learning compact relational features withou...

Probabilistic Combination of Features for Music Classification

We describe an approach to the combination of music similarity feature spaces in the context of music classification. The approach is based on taking the product of posterior probabilities obtained from separate classifiers for the different feature spaces. This allows for a different influence of the classifiers per song and an overall classification accuracy improving those resulting from ind...

Probabilistic Analysis of Projected Features in Binocular Stereo