We study inference and learning based on a sparse coding model with 'spike-and-slab'prior.
As in standard sparse coding, the model used assumes independent latent sources that …

Model compensation is a standard way of improving the robustness of speech recognition
systems to noise. A number of popular schemes are based on vector Taylor series (VTS) …

Discriminative classifiers are a popular approach to solving classification problems.
However, one of the problems with these approaches, in particular kernel based classifiers …

Noise robustness is a key issue in successful deployment of automatic speech recognition
systems in demanding environments such as hospital operating rooms. Perhaps the most …

A standard way of improving the robustness of speech recognition systems to noise is model
compensation. This replaces a speech recogniser's distributions over clean speech by ones …

Discriminative classifiers are a popular approach to solving classification problems.
However one of the problems with these approaches, in particular kernel based classifiers …

In this paper, a comparison of three fundamentally different noise robust approaches is
carried out. The recognition performances of multicondition training, Data-driven Parallel …

Model compensation methods for noise-robust speech recognition have shown good
performance. Predictive linear transformations can approximate these methods to balance …

Abstract Model compensation is a standard way of improving the robustness of speech
recognition systems to noise. A number of popular schemes are based on vector Taylor …

Sparse coding is a widely applied latent variable analysis technique. The standard
formulation of sparse coding assumes Laplace as a prior distribution for modeling the …