Minimum information required for written word recognition

International audienceReading is an automated process. One of the remarkable human abilities is that we can read even partially erased or hidden words. We carried out a study on written word recognition in order to decipher how much information is required at least to identify a word. Experimental software was designed in C++ language to measure the amount of information in pixels and reaction time. The results showed we could identify words at a very low display rate and suggest that prior knowledge on the category of words play a mediating role in written word recognition

Perception and comprehension of linguistic and affective prosody in children with Landau-Kleffner syndrome

International audienceThe present study investigated language outcomes in children with Landau-Kleffner syndrome compared with 7 to 8 year-old healthy children and healthy adults. We examined their capacity of understanding simple sentences using linguistic and affective prosodic cues and perceiving them. A battery of prosodic tests was elaborated and used for this study. Results revealed certain delayed language development or a different pattern of performance in participants with Landau-Kleffner syndrome. With more subjects tested in the future results from our battery of prosodic tests would allow us to better understand language development in child and it would be helpful for speech-language therapies

From the perception of emotion to their expression: a study on linguistic and affective prosody problems in French speaking high functioning autistic children

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A novel prosody assessment test: Findings in three cases of Landau–Kleffner syndrome

International audienceLandau–Kleffner syndrome (LKS) is a rare childhood neurological disorder characterized by subacute aphasia, auditory agnosia and abnormal EEG. Prosody structures utterances, indicates sentence modality (linguistic prosody) and expresses the speaker’s intention, attitude and emotions (affective prosody). It not only functions as (para-) linguistic features but also provides social explanations. It has been shown that infants can perceive, distinguish and use prosodic features for communication. Since patients with LKS have no more means of verbal communication, we suppose they use some “primitive” cues in an attempt to understand what is said to them. Based on the fact that aphasia does not mean loss of social capacity/functionality, and the precociousness and functions of prosody, the present study investigated prosodic capacity outcomes (possible preservation of prosody) in three individuals - two children and one adolescent - with LKS compared with 7–8 yearold healthy children and healthy adults. A set of perceptual tests of linguistic and affective prosody was elaborated and used for this study. Results revealed that affective prosody is better used in a child with LKS than in the control group under the conditions such as relatively late age at onset, short duration of epilepsy/medication and persistent comprehension problems. Given that prosody appears to be helpful for better oral comprehension, prosody should be used in speech therapy for children with LKS

A connectionist model of reading with error correction properties

International audienceRecent models of associative long term memory (LTM) have emerged in the field of neuro-inspired computing. These models have interesting properties of error correction, robustness, storage capacity and retrieval performance. In this context, we propose a connectionist model of written word recognition with correction properties, using associative memories based on neural cliques. Similarly to what occurs in human language, the model takes advantage of the combination of phonological and orthographic information to increase the retrieval performance in error cases. Therefore, the proposed architecture and principles of this work could be applied to other neuro-inspired problems that involve multimodal processing, in particular for language applications

Analyse automatique du discours de patients pour la détection de comorbidités psychiatriques

International audienceCo-morbidities are very frequent in mental health and represent a major therapeutic issueas well as a lever for a better understanding of physiopathological mechanisms. Some diseases,such as schizophrenia, develop progressively and at different rates from one individualto another, with symptoms gradually increasing in intensity and specificity. In these cases,clinicians seek to identify early on the warning symptoms and aggravating comorbidities, inorder to propose interventions that maximize the therapeutic effects. Speech, and thus language,is a key element they use during consultations to understand the psychological state ofpatients, and automatic language analysis systems can provide an aid to assessment. We proposesuch an aid, targeting the detection of comorbidities and based on dependency grammarsand paralinguistic indicators such as pauses and interjections, which are shown to be relevant.Les comorbidités sont très fréquentes en santé mentale et représentent un enjeu thérapeutique majeur ainsi qu'un levier pour une meilleure compréhension des mécanismes physiopathologiques des pathologies. Certains maladies, comme la schizophrénie, s'installent progressivement et de façon variable d'un individu à l'autre, les symptômes augmentant progressivement en intensité et en spécificité. Dans ces cas, les cliniciens cherchent à identifier au plus tôt les symptômes annonciateurs et les comorbidités associés, afin de proposer des interventions maximisant les effets thérapeutiques. La parole, et donc le langage, est un élément-clé sur lequel ils s'appuient lors des consultations pour comprendre l'état psychique des patients, et les systèmes d'analyse automatique de la langue peuvent fournir une aide à l'évaluation. Nous proposons une telle aide, ciblant la détection de comorbidités et fondée sur les grammaires de dépendances et des indicateurs paralinguistiques comme les pauses et les interjections, qui s'avèrent être des choix pertinents

Using Dependency Syntax-Based Methods for Automatic Detection of Psychiatric Comorbidities

International audienceThis paper presents the early stages of a growing corpus of psychiatric interviews from help seeking patients referred to an early detection and intervention center for psychosis. In order to contribute to the practitioner's diagnostic, we focus on a new method of automatic comorbidity detection in the corpus. Among the novelties of this method is the fact that it is based on syntactic features of paralinguistic data (interjections and pauses). We use the formalism of dependency syntax, a brief description of which we provide in the paper. Considering the (currently) small size of the corpus, our intention is to prove the applicability of the method rather than to obtain general results about the relevance of syntactic indicators