We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
This paper shows that incorporating linguistically motivated features to ensure correct animacy and number agreement in an averaged perceptron ranking model for CCG realization he...
This paper presents a non-parallel training algorithm for voice conversion based on feature transform Gaussian mixture model (FTGMM), which is a mixture model of joint density spa...
The method which is called the “tandem approach” in speech recognition has been shown to increase performance by using classifier posterior probabilities as observations in a...
In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood ...