The challenge of MNCs and development: oil extraction, CSR, Nigeria and corruption

Globalisation has led to a shift in world order with the rise of the corporate non-state actor. This rise has led to an assumption that Multi-National Corporations (MNCs) must assume responsibilities beyond profit maximisation for shareholders. With the rise of the MNC as a corporate non-state actor there have been discussions around the role of business with regard to human rights.

This article looks at the case of oil extraction in Nigeria. Focussing on the historical dependency of Nigeria and the evolution of the state into a resource-dependent country, it looks at the limitations of existing human rights obligations as they relate to business. This article proposes that corporate social responsibility (CSR) policies of MNCs can act as a preliminary stage in the quest for wider human rights protections. It is in motivating MNCs to design and implement effective CSR policies in dependent states like Nigeria, that the challenge lies.

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.