Authoritarian Responses to Foreign Pressure: Spending, Repression, and Sanctions

This paper explores how international sanctions affect authoritarian rulers’ decisions concerning repression and public spending composition, and how different authoritarian rulers respond to foreign pressure. If sanctions are assumed to increase the price of loyalty to the regime, then rulers whose budgets are not severely constrained by sanctions will tend to increase spending in those categories that most benefit their core support groups. In contrast, when constraints are severe due to reduced aid and trade, dictators are expected to greatly increase their levels of repression. Using data on regime types, public expenditures and spending composition (1970–2000) as well as on repression levels (1976–2001), we show that the empirical patterns conform well to our theoretical expectations. Single-party regimes, when targeted by sanctions, increase spending on subsidies and transfers which largely benefit more substantial sectors of the population and especially the urban classes. Likewise, military regimes increase their expenditures on goods and services, which include military equipment and soldiers’ and officers’ wages. Conversely, personalist regimes reduce spending in all categories, especially capital expenditures, while increasing repression much more than other regime types when targeted by sanctions.

Real-Time implementation of a blind authentication method using self-synchronous speech watermarking

A blind speech watermarking scheme that meets hard real-time deadlines is presented and implemented. In addition, one of the key issues in these block-oriented watermarking techniques is to preserve the synchronization. Namely, to recover the exact position of each block in the mark extract process. In fact, the presented scheme can be split up into two distinguished parts, the synchronization and the information mark methods. The former is embedded into the time domain and it is fast enough to be run meeting real-time requirements. The latter contains the authentication information and it is embedded into the wavelet domain. The synchronization and information mark techniques are both tunable in order to allow a con gurable method. Thus, capacity, transparency and robustness can be con gured depending on the needs. It makes the scheme useful for professional applications, such telephony authentication or even sending information throw radio applications.

On the origin and scope of the second language speech production disadvatage

Los hablantes bilingües tienen un acceso al léxico más lento y menos robusto que los monolingües, incluso cuando hablan en su lengua materna y dominante. Este fenómeno, comúnmente llamado “la desventaja bilingüe” también se observa en hablantes de una segunda lengua en comparación con hablantes de una primera lengua. Una causa que posiblemente contribuya a estas desventajas es el uso de control inhibitorio durante la producción del lenguaje: la inhibición de palabras coactivadas de la lengua actualmente no en uso puede prevenir intrusiones de dicha lengua, pero al mismo tiempo ralentizar la producción del lenguaje. El primer objetivo de los estudios descritos en este informe era testear esta hipótesis mediante diferentes predicciones generadas por teorías de control inhibitorio del lenguaje. Un segundo objetivo era investigar la extensión de la desventaja bilingüe dentro y fuera de la producción de palabras aisladas, así como avanzar en el conocimiento de las variables que la modulan. En lo atingente al primer objetivo, la evidencia obtenida es incompatible con un control inhibitorio global, desafiando la idea de mecanismos específicos en el hablante bilingüe utilizados para la selección léxica. Esto implica que una explicación común para el control de lenguaje y la desventaja bilingüe en el acceso al léxico es poco plausible. En cuanto al segundo objetivo, los resultados muestran que (a) la desventaja bilingüe no tiene un impacto al acceso a la memoria; (b) la desventaja bilingüe extiende a la producción del habla conectada; y (c) similitudes entre lenguas a diferentes niveles de representación así como la frecuencia de uso son factores que modulan la desventaja bilingüe.   

Late bloomer or language impaired? Screening the speech of a Spanish-English bilingual toddler for early signs of language impairment

This case study presents corpus data gathered from a Spanish-English bilingual child with expressive language delay. Longitudinal data on the child’s linguistic development was collected from the onset of productive speech at age 1;1 until age 4 over the course of 28 video-taped sessions with the child’s principal caregivers. A literature review focused on the relationship between language delay and persisting disorders—including a discussion of the frequent difficulty in distinguishing between the two at early stages of bilingual development—is followed by an analysis of the child’s productive development in 2 distinct phases. An attempt is made to assess the child’s speech at age 4 for preliminary signs of SLI and to consider techniques for identifying ‘at risk’ bilingual children (that is, those with productive language delay, poor oral fluency, and family history of language problems) based on samples of recorded and transcribed speech.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Multimodal Strategies in Development.Predictive Value of early simultaneosgesture-speech comination

The present study investigates the predictive value of the early appearance of simultaneous pointing-speech combinations. An experimental task was used to obtain a communicative productive sample from nineteen children at 1;0 and 1;3. Infant’s communicative productions, in combination with gaze joint engagement patterns, were analyzed in relation to different social conditions. The results show a significant effect of age and social condition on infants’ communicative productions. Gesture-speech combinations seem to work as a strong communicative resource to attract the adult’s attention in social demanding communicative contexts. Gaze joint engagement was used in combination with simultaneous pointing-speech combinations to attract adults’ attention during social demanding conditions. Finally, the use of simultaneous pointing-speech combinations at 1;0 in demanding conditions predicted greater expressive vocabulary acquisition at 1;3 and 1;6. These results indicate that the use of gesture-speech combinations may be considered a significant step towards the early integration of language components.

Reconciling phonological neighborhood effects in speech production through single trial analysis

A crucial step for understanding how lexical knowledge is represented is to describe the relative similarity of lexical items, and how it influences language processing. Previous studies of the effects of form similarity on word production have reported conflicting results, notably within and across languages. The aim of the present study was to clarify this empirical issue to provide specific constraints for theoretical models of language production. We investigated the role of phonological neighborhood density in a large-scale picture naming experiment using fine-grained statistical models. The results showed that increasing phonological neighborhood density has a detrimental effect on naming latencies, and re-analyses of independently obtained data sets provide supplementary evidence for this effect. Finally, we reviewed a large body of evidence concerning phonological neighborhood density effects in word production, and discussed the occurrence of facilitatory and inhibitory effects in accuracy measures. The overall pattern shows that phonological neighborhood generates two opposite forces, one facilitatory and one inhibitory. In cases where speech production is disrupted (e.g. certain aphasic symptoms), the facilitatory component may emerge, but inhibitory processes dominate in efficient naming by healthy speakers. These findings are difficult to accommodate in terms of monitoring processes, but can be explained within interactive activation accounts combining phonological facilitation and lexical competition.

Bootstrapping a statistical speech translator from a rule-based one

We describe a series of experiments in which we start with English to French and English to Japanese versions of an Open Source rule-based speech translation system for a medical domain, and bootstrap correspondign statistical systems. Comparative evaluation reveals that the rule-based systems are still significantly better than the statistical ones, despite the fact that considerable effort has been invested in tuning both the recognition and translation components; also, a hybrid system only marginally improved recall at the cost of a los in precision. The result suggests that rule-based architectures may still be preferable to statistical ones for safety-critical speech translation tasks.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

On cumulant techniques in speech processing

This paper analyzes applications of cumulant analysis in speech processing. A special focus is made on different second-order statistics. A dominant role is played by an integral representation for cumulants by means of integrals involving cyclic products of kernels.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.