Freedom of Expression and Hate Speech

This is a study of free speech and hate speech with reference to the international standards and to the United States jurisprudence. The study, in a comparative and critical fashion, depicts the historical evolution and the application of the concept of ‘free speech,’ within the context of ‘hate speech.’ The main question of this article is how free speech can be discerned from hate speech, and whether the latter should be restricted. To this end, it examines the regulation of free speech under the First Amendment to the United States Constitution, and in light of the international standards, particularly under the International Convention on the Elimination of All Forms of Racial Discrimination, International Covenant on Civil and Political Rights, and the European Convention on Human Rights and Fundamental Freedoms. The study not only illustrates how elusive the endeavour of striking a balance between free speech and other vital interests could be, but also discusses whether and how hate speech should be eliminated within the ‘marketplace of ideas.’

Emotional speech: towards a new generation of databases

Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect of substantial applications, notably in human-computer interaction. Progress in the area relies heavily on the development of appropriate databases. This paper addresses the issues that need to be considered in developing databases of emotional speech, and shows how the challenge of developing apropriate databases is being addressed in three major recent projects - the Belfast project, the Reading-Leeds project and the CREST-ESP project. From these and other studies the paper draws together the tools and methods that have been developed, addresses the problems that arise and indicates the future directions for the development of emotional speech databases.

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.