The German Federal Constitutional Court's Ruling on Outright Monetary Transactions (OMT) - Another Step Towards National Closure?

The German Federal Constitutional Court (FCC) ruling of 14 January 2014 deserves a thorough evaluation on several accounts: It is the first ever reference by the FCC to the Court of Justice of the European Union (CJEU), it represents a continuation of FCC case law aimed at restricting the impact of European Union law as interpreted by the Court of Justices of the European Union (CJEU) on German law as well as questioning Germany’s participation in an ever closer European Union, and it has the potential to dictate the future course of the EU’s Economic and Monetary Union (EMU).

This case note discusses three aspects of this decision. First, it considers the aims of challenging the youngest measures to contain the euro currency crisis before the FCC, focusing on the question in how far the claims are based on national closure as opposed to an ever closer union of the peoples of Europe. Secondly it analyzes in how far the aims the claims pursue are reflected in the FCC’s response. Thirdly, it considers the substantive relevance of this reference, highlighting the surprisingly vague consequences the FCC envisages should the CJEU not re-interpret the OMT decision as the FCC suggests, and illuminating the strategic aims of the reference without deference. In conclusion, it sketches the remaining scope for the EU to engage in or at least facilitate transnational solidarity.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.