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.

A detailed investigation of classification methods for vowel speech imagery recognition

Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.

A Game-Theoreteic Analysis of Minority Language Use in Multilingual Societies

This chapter studies multilingual democratic societies with highly developed economies. These societies are assumed to have two languages with official status: language A, spoken by every individual, and language B, spoken by the bilingual minority. We emphasize that language rights are important, but the survival of the minority language B depends mainly on the actual use bilinguals make of B. The purpose of the present chapter is to study some of the factors affecting the bilingual speakers language choice behaviour. Our view is that languages with their speech communities compete for speakers just as fi rms compete for market share. Thus, the con ict among the minority languages in these societies does not take the rough expressions such as those studied in Desmet et al. (2012). Here the con flict is more subtle. We model highly plausible language choice situations by means of choice procedures and non-cooperative games, each with different types of information. We then study the determinants of the bilinguals ' strategic behaviour with regard to language. We observe that the bilinguals' use of B is shaped, essentially, by linguistic conventions and social norms that are developed in situations of language contact.