Speech production in Parkinson's disease: II. Acoustic and electropalatographic investigation of sentence, word and segment durations

Previous investigations employing electropalatography (EPG) have identified articulatory timing deficits in individuals with acquired dysarthria. However, this technology is yet to be applied to the articulatory timing disturbance present in Parkinson's disease (PD). As a result, the current investigation aimed to use EPG to comprehensively examine the temporal aspects of articulation in a group of nine individuals with PD at sentence, word and segment level. This investigation followed on from a prior study (McAuliffe, Ward and Murdoch) and similarly, aimed to compare the results of the participants with PD to a group of aged (n=7) and young controls (n=8) to determine if ageing contributed to any articulatory timing deficits observed. Participants were required to read aloud the phrase I saw a ___ today'' with the EPG palate in-situ. Target words included the consonants /1/, /s/ and /t/ in initial position in both the /i/ and /a/ vowel environments. Perceptual investigation of speech rate was conducted in addition to objective measurement of sentence, word and segment duration. Segment durations included the total segment length and duration of the approach, closure/constriction and release phases of EPG consonant production. Results of the present study revealed impaired speech rate, perceptually, in the group with PD. However, this was not confirmed objectively. Electropalatographic investigation of segment durations indicated that, in general, the group with PD demonstrated segment durations consistent with the control groups. Only one significant difference was noted, with the group with PD exhibiting significantly increased duration of the release phase for /1a/ when compared to both the control groups. It is, therefore, possible that EPG failed to detect lingual movement impairment as it does not measure the complete tongue movement towards and away from the hard palate. Furthermore, the contribution of individual variation to the present findings should not be overlooked.

Self-paced reading and sentence comprehension in Parkinson's disease

Parkinson's disease (PD) is associated with disturbances in sentence processing, particularly for noncanonical sentences. The present study aimed to analyse sentence processing in PD patients and healthy control participants, using a word-by-word self-paced reading task and an auditory comprehension task. Both tasks consisted of subject relative (SR) and object relative (OR) sentences, with comprehension accuracy measured for each sentence type. For the self-paced reading task, reading times (RTs) were also recorded for the non-critical and critical processing regions of each sentence. Analysis of RTs using mixed linear model statistics revealed a delayed sensitivity to the critical processing region of OR sentences in the PD group. In addition, only the PD group demonstrated significantly poorer comprehension of OR sentences compared to SR sentences during an auditory comprehension task. These results may be consistent with slower lexical retrieval in PD, and its influence on the processing of noncanonical sentences. (c) 2005 Elsevier Ltd. All rights reserved.

From filters to features:Scale-space analysis of edge and blur coding in human vision

To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.

Chronotopographical analysis of the pattern onset visual evoked magnetic response (VEMR):Implications for waveform peak identification

The topography of the visual evoked magnetic response (VEMR) to a pattern onset stimulus was studied in five normal subjects using a single channel BTi magnetometer. Topographic distributions were analysed at regular intervals following stimulus onset (chronotopograpby). Two distinct field distributions were observed with half field stimulation: (1) activity corresponding to the C11 m which remains stable for an average of 34 msec and (2) activity corresponding to the C111 m which remains stable for about 50 msec. However, the full field topography of the largest peak within the first 130 msec does not have a predictable latency or topography in different subjects. The data suggest that the appearance of this peak is dependent on the amplitude, latency and duration of the half field C11 m peaks and the efficiency of half field summation. Hence, topographic mapping is essential to correctly identify the C11 m peak in a full field response as waveform morphology, peak latency and polarity are not reliable indicators. © 1993.

Comparing German and British political culture through values : and analysis of the values of health care reform

This research sets out to compare the values in British and German political discourse, especially the discourse of social policy, and to analyse their relationship to political culture through an analysis of the values of health care reform. The work proceeds from the hypothesis that the known differences in political culture between the two countries will be reflected in the values of political discourse, and takes a comparison of two major recent legislative debates on health care reform as a case study. The starting point in the first chapter is a brief comparative survey of the post-war political cultures of the two countries, including a brief account of the historical background to their development and an overview of explanatory theoretical models. From this are developed the expected contrasts in values in accordance with the hypothesis. The second chapter explains the basis for selecting the corpus texts and the contextual information which needs to be recorded to make a comparative analysis, including the context and content of the reform proposals which comprise the case study. It examines any contextual factors which may need to be taken into account in the analysis. The third and fourth chapters explain the analytical method, which is centred on the use of definition-based taxonomies of value items and value appeal methods to identify, on a sentence-by-sentence basis, the value items in the corpus texts and the methods used to make appeals to those value items. The third chapter is concerned with the classification and analysis of values, the fourth with the classification and analysis of value appeal methods. The fifth chapter will present and explain the results of the analysis, and the sixth will summarize the conclusions and make suggestions for further research.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.