Le dialogisme intertextuel des contes des Grimm

Ute Heidmann Le dialogisme intertextuel des contes des Grimm Préalables pour une enquête à mener « Le caractère le plus important de l'énoncé, ou en tous les cas le plus ignoré, est son dialogisme, c'est-à-dire sa dimension intertextuelle », constate Todorov en référence à la conception dialogique du langage proposée par Bakthine. Cet article introductif postule que ce constat s'applique aussi aux contes des Grimm. En partant des recherches déjà menées sur Apulée, Straporola, Basile, Perrault, La Fontaine et Lhéritier*, il présente des concepts (réponse intertextuelle, reconfiguration générique et scénographie en trompe-l'oeil) dont il illustre l'efficacité pour l'analyse des Kinder- und Hausmärchen. L'analyse de la préface de 1812 montre que les Grimm créent une scénographie pour légitimer le genre des Kinder- und Hausmärchen en les présentant comme des contes "d'origine" qui auraient "poussé" comme des plantes dans leur région et qu'ils n'auraient fait que "collecter". Cette scénographie en trompe-l'oeil permet de dissimuler le fort impact des contes européens et notamment français sur les Kinder- und Hausmärchen. Leurs commentaires paratextuels permettent en revanche de retracer ces dialogues intertextuels qui ne se limitent pas à imiter les "voix déjà présentes dans le choeur complexe" des narrateurs des contes déjà racontés, mais qui créent des effets de sens nouveaux et significativement différents en guise de réponse aux "histoires ou contes du passé", comme l'avaient déjà fait Charles Perrault avant eux. *(dans Féeries 8 et Textualité et intertextualité des contes, Editions Classiques Garnier 2010) "The most important feature of the utterance, or at least the most neglected, is its dialogism, that is, its intertextual dimension" states Todorov in reference to Bakthin's dialogical conception of human speech. Ute Heidmann's introductory essay argues that this applies also to the Grimm's tales. Extending her former theoretical and intertextual investigation on Apuleius, Straporala, Basile, Perrault, La Fontaine and Lhéritier*, she proposes a series of conceptual options (as intertextual response, scenography, trompe l'oeil, generic reconfiguration, discursive strategy) that can efficiently be used for the work on the Kinder- und Hausmärchen, gesammelt durch die Brüder Grimm. The article shows how the Grimms skilfully construct a highly suggestive scenography and topography for the new generic form thus creating the idea of a genuine tale, having grown naturally in the earth of their own region and how it is efficiently used to dissimulate the strong impact of the European and namely the French fairy tales on the Grimm's tales. The extensive paratextual commentaries are shown to serve the same purpose. Once these strategies are "deconstructed" as such, the way is free to trace the very complex intertextual dialogues with already existing Italian, French, German tales, that underlie the Kinder- und Hausmärchen. Comparative textual analysis can then make us discover, that these dialogues are from just "imitating" "the many other voices already present in the complex chorus" of fairy tale writers and narrators: they actually create new and different meaning by responding to them. * (in Féeries 8, Textualité et intertextualité des contes, Classiques Garnier 2010)

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.