In this chapter we describe deep generative and discriminative models as they have been applied to speech recognition and related pattern recognition problems. The former models describe the distribution of data or the joint distribution of data and the corresponding targets, whereas the latter models describe the distribution of targets conditioned on data. Both models are characterized as bei...

The complexity of motions in the environment preWe propose a novel method for temporally and spatially corresponding moving objects by automatically learning the relevance of the objects’ appearance features to the task of discrimination. Efficient correspondence is achieved by enforcing temporal consistency of the relevances for a particular object. Relevances are learned using a technique we ...

We study a novel architecture for syntactic SMT. In contrast to the dominant approach in the literature, the system does not rely on translation rules, but treat translation as an unconstrained target sentence generation task, using soft features to capture lexical and syntactic correspondences between the source and target languages. Target syntax features and bilingual translation features ar...

„ Generative probability models deal with missing information and variable length sequences.

Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applicability and success in image processing. Although plenty of effort has been made in designing and training better discriminative CNNs, little is yet known about the internal features these models learn. Questions like, what specific knowledge is coded within CNN layers, and how can it be used for ...

We present log-linear mixture models as a fully discriminative approach to object category recognition which can, analogously to kernelised models, represent non-linear decision boundaries. We show that this model is the discriminative counterpart to Gaussian mixtures and that either one can be transformed into the respective other. However, the proposed model is easier to extend toward fusing ...

Due to the scarcity of labeled data, most melody extraction algorithms do not rely on fully data-driven processing blocks but rather on careful engineering. For example, the Melodia melody extraction algorithm employs a pitch contour selection stage that relies on a number of heuristics for selecting the melodic output. In this paper we explore the use of a discriminative model to perform purel...