Neural networks for nonlinear discriminant analysis in continuous speech recognition

In this paper neural networks for Nonlinear Discriminant Analysis in continuous speech recognition are presented. Multilayer Perceptrons are used to estimate a-posteriori probabilities for Hidden-Markov Model states, which are the optimal discriminant features for the separation of the HMM states. The a-posteriori probabilities are transformed by a principal component analysis to calculate the new features for semicontinuous HMMs, which are trained by the known Maximum-Likelihood training. The nonlinear discriminant transformation is used in speaker-independent phoneme recognition experiments and compared to the standard Linear Discriminant Analysis technique

‘Launching’ a new nation: The unfolding brand of South Sudan

In June 2011 South Sudan joined the United Nations as a new state, marking the final stage of years of struggle for independence. Its secession is witness to the potential of regions with no historic claims of statehood to achieve independence. This unique situation gives researchers the opportunity to explore how the brand of a nation comes into existence. This article traces how the brand of a new nation has unfolded in the case of South Sudan and analyzes how that brand was written into existence by international news media. This exploratory case study approach is based on inductive content analysis research processes founded on grounded theory. The research brings new insights to research of nation brands in their very early stages