Financial phantasmagoria: corporate image-work in times of crisis

Our purpose in this article is to relate the real movements in the economy during 2008 to the ?image-work? of financial institutions. Over the period January?December 2008 we collected 241 separate advertisements from 61 financial institutions published in the Financial Times. Reading across the ensemble of advertisements for themes and evocative images provides an impression of the financial imaginaries created by these organizations as the global financial crisis unfolded. In using the term ?phantasmagoria? we move beyond its colloquial sense of a set of strange images designed to dazzle towards the more technical connotation used by Ranci�re (2004) who suggested that words and images can offer a trace of an overall determining set-up if they are torn from their obviousness so they become phantasmagoric figures. The key phantasmagoric figure we identify here is that of the financial institution as timeless, immortal and unchanging; a coherent and autonomous entity amongst other actors. This notion of uniqueness belies the commonality of thinking which precipitated the global financial crisis as well as the limited capacity for control of financial institutions in relation to market events. It also functions as a powerful naturalizing force, making it hard to question certain aspects of the recent period of ?capitalism in crisis?

Bilingually motivated word segmentation for statistical machine translation

We introduce a bilingually motivated word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Our approach is motivated from the insight that PB-SMT systems can be improved by optimizing the input representation to reduce the predictive power of translation models. We firstly present an approach to optimize the existing segmentation of both source and target languages for PB-SMT and demonstrate the effectiveness of this approach using a Chinese–English MT task, that is, to measure the influence of the segmentation on the performance of PB-SMT systems. We report a 5.44% relative increase in Bleu score and a consistent increase according to other metrics. We then generalize this method for Chinese word segmentation without relying on any segmenters and show that using our segmentation PB-SMT can achieve more consistent state-of-the-art performance across two domains. There are two main advantages of our approach. First of all, it is adapted to the specific translation task at hand by taking the corresponding source (target) language into account. Second, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains

Pitch Comparisons between Electrical Stimulation of a Cochlear Implant and Acoustic Stimuli Presented to a Normal-hearing Contralateral Ear

Four cochlear implant users, having normal hearing in the unimplanted ear, compared the pitches of electrical and acoustic stimuli presented to the two ears. Comparisons were between 1,031-pps pulse trains and pure tones or between 12 and 25-pps electric pulse trains and bandpass-filtered acoustic pulse trains of the same rate. Three methods—pitch adjustment, constant stimuli, and interleaved adaptive procedures—were used. For all methods, we showed that the results can be strongly influenced by non-sensory biases arising from the range of acoustic stimuli presented, and proposed a series of checks that should be made to alert the experimenter to those biases. We then showed that the results of comparisons that survived these checks do not deviate consistently from the predictions of a widely-used cochlear frequency-to-place formula or of a computational cochlear model. We also demonstrate that substantial range effects occur with other widely used experimental methods, even for normal-hearing listeners

Minimum Exact Word Error Training

In this paper we present the Minimum Exact Word Error (exactMWE) training criterion to optimise the parameters of large scale speech recognition systems. The exactMWE criterion is similar to the Minimum Word Error (MWE) criterion but uses the exact word error instead of an approximation based on time alignments as used in the MWE criterion. It is shown that the exact word error for all word sequence hypotheses can be represented on a word lattice. This can be accomplished using transducer-based methods. The result is a word lattice of slightly refined topology. The accumulated weights of each path through such a lattice then represent the exact number of word errors for the corresponding word sequence hypothesis. Using this compressed representation of the word error of all word sequences represented in the original lattice, exactMWE can be performed using the same latticebased re-estimation process as for MWE training. First experiments on the Wall Street Journal dictation task do not show significant differences in recognition performance between exactMWE and MWE at comparable computational complexity and convergence behaviour of the training. 1

Improving Automatic Speech Recognition Using Tangent Distance

In this paper we present a new approach to variance modelling in automatic speech recognition (ASR) that is based on tangent distance (TD). Using TD, classifiers can be made invariant w.r.t. small transformations of the data. Such transformations generate a manifold in a high dimensional feature space when applied to an observation vector. While conventional classifiers determine the distance between an observation and a prototype vector, TD approximates the minimum distance between their manifolds, resulting in classification that is invariant w.r.t. the underlying transformation. Recently, this approach was successfully applied in image object recognition. In this paper we describe how TD can be incorporated into ASR systems based on Gaussian mixture densities (GMD). The proposed method is embedded into a probabilistic framework. Experiments performed on the SieTill corpus for telephone line recorded German digit strings show a significant improvement in comparison with a conventional GMD approach using a comparable amount of model parameters. 1