Agency and Appetite for religious Song: Vexilla regis prodeunt and its first thousand years

When James Joyce made two of his characters in ‘‘Portrait of an Artist as a Young Man’’ refer approvingly to ‘‘Vexilla regis prodeunt’’ he was following in the footsteps of a long line of the Latin text’s admirers. Since Anglo-Saxon times English audiences had clearly appreciated the sonorous majesty of this processional hymn, largely because of the solemnity and craft with which it celebrated the nature of Christ’s martial triumph and sacrifice. This article offers a snapshot of different kinds of English appetite for Venantius Fortunatus’ famous religious song for the first thousand years of its existence, from the Anglo-Saxon period through to the mid sixteenth century.

Ethno-Religious Segregation in Post-Conflict Belfast

The last 15 years have seen ethno-religious segregation in Belfast stabilize as mixed residential neighbourhoods have expanded on the back of peace and political stability. However, the recession has exposed some of the fragility of these changes and in particular the overreliance on property-led growth and the housing market to achieve lasting forms of desegregation. This paper examines the nature of sociocultural spatial change and in particular how uneven urban restructuring has privileged the south of the city at the expense of the inner east, north and west. The paper concludes by highlighting the implications for housing policy and planning skills both regionally and nationally.

The Importance of Religious Displays for Belief Acquisition and Secularization

Both the sociology and the cognitive science of religion seek to explain the acquisition of religious beliefs. In this article, I offer an account of the acquisition and distribution of religious beliefs using the findings of both fields. In the process, I seek to illustrate the potential of interdisciplinary dialogue for improving our understanding of religion and its absence. More specifically, I present a prima facie case—based on existing work in the social and cognitive sciences, exploratory online surveys, and participant observation—that witnessing actions attesting to religious claims is one of the most crucial variables determining whether or not an individual will explicitly believe such claims. Further, I argue that the connection between action and belief can help produce an improved account of secularization and non-theism, defined here as the lack of explicit belief in the existence of non-physical agents.

The Science of Religious Beliefs

Why have humans, throughout history and across cultures, shown a strong tendency to believe in the existence of superhuman intentional agents and attached this belief to notions of morality, misfortune, and the creation of the world? The answer emerging from the cognitive science of religion appears to be that explicit beliefs are informed and constrained by the natural and cross-culturally recurrent operation of implicit cognitive systems. Successful god concepts resonate with the expectations of these implicit systems but also have attention-demanding and inferentially-rich properties that allow their integration into various areas of human concern. Theological concepts may deviate from these natural cognitive moorings but require special cultural scaffolding, such as Whitehouse's two Modes of Religiosity, to do so and constitute additions to, rather than replacements of the religious beliefs supported by implicit cognitive systems.

IMPROVED POSTFILTERING TECHNIQUE FOR BLOCK QUANTISATION OF SPEECH.

A modified comb filtering technique is proposed which can be used to reduce framing noise generated when speech signals are transform-coded or vector-quantized. Application of this filter to 9. 6 kbit/s speech in a vector transform coder has been found to improve the perceptual quality of the coded speech.

Application of artificial neural network techniques to low bit-rate speech coding

Research has been undertaken to investigate the use of artificial neural network (ANN) techniques to improve the performance of a low bit-rate vector transform coder. Considerable improvements in the perceptual quality of the coded speech have been obtained. New ANN-based methods for vector quantiser (VQ) design and for the adaptive updating of VQ codebook are introduced for use in speech coding applications.

Optimization of Weighted Finite State Transducer for Speech Recognition

There is considerable interest in creating embedded, speech recognition hardware using the weighted finite state transducer (WFST) technique but there are performance and memory usage challenges. Two system optimization techniques are presented to address this; one approach improves token propagation by removing the WFST epsilon input arcs; another one-pass, adaptive pruning algorithm gives a dramatic reduction in active nodes to be computed. Results for memory and bandwidth are given for a 5,000 word vocabulary giving a better practical performance than conventional WFST; this is then exploited in an adaptive pruning algorithm that reduces the active nodes from 30,000 down to 4,000 with only a 2 percent sacrifice in speech recognition accuracy; these optimizations lead to a more simplified design with deterministic performance.

Robust Audio-Visual Speech Recognition under Noisy Audio-Video Conditions

This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption. A significant advantage of MWSP is that it does not require any specific measurements of the signal in either stream to calculate appropriate stream weights during recognition, and as such it is modality-independent. This also means that MWSP complements and can be used alongside many of the other approaches that have been proposed in the literature for this problem. For evaluation we used the large XM2VTS database for speaker-independent audio-visual speech recognition. The extensive tests include both clean and corrupted utterances with corruption added in either/both the video and audio streams using a variety of types (e.g., MPEG-4 video compression) and levels of noise. The experiments show that this approach gives excellent performance in comparison to another well-known dynamic stream weighting approach and also compared to any fixed-weighted integration approach in both clean conditions or when noise is added to either stream. Furthermore, our experiments show that the MWSP approach dynamically selects suitable integration weights on a frame-by-frame basis according to the level of noise in the streams and also according to the naturally fluctuating relative reliability of the modalities even in clean conditions. The MWSP approach is shown to maintain robust recognition performance in all tested conditions, while requiring no prior knowledge about the type or level of noise.

A Corpus-Based Approach to Speech Enhancement from Nonstationary Noise

Temporal dynamics and speaker characteristics are two important features of speech that distinguish speech from noise. In this paper, we propose a method to maximally extract these two features of speech for speech enhancement. We demonstrate that this can reduce the requirement for prior information about the noise, which can be difficult to estimate for fast-varying noise. Given noisy speech, the new approach estimates clean speech by recognizing long segments of the clean speech as whole units. In the recognition, clean speech sentences, taken from a speech corpus, are used as examples. Matching segments are identified between the noisy sentence and the corpus sentences. The estimate is formed by using the longest matching segments found in the corpus sentences. Longer speech segments as whole units contain more distinct dynamics and richer speaker characteristics, and can be identified more accurately from noise than shorter speech segments. Therefore, estimation based on the longest recognized segments increases the noise immunity and hence the estimation accuracy. The new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the corpus speech, and an algorithm to identify the longest matching segments between the noisy sentence and the corpus sentences. The algorithm is made more robust to noise uncertainty by introducing missing-feature based noise compensation into the corpus sentences. Experiments have been conducted on the TIMIT database for speech enhancement from various types of nonstationary noise including song, music, and crosstalk speech. The new approach has shown improved performance over conventional enhancement algorithms in both objective and subjective evaluations.