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Contrary to what is stated in much of the literature which is based in large part on Parisian French, many dialects of French still have long and short vowels (e.g. in Switzerland and Belgium). This study had two aims. The first was to show that Swiss French speakers, as opposed to Parisian French speakers, produce long vowels with durations that are markedly different from those of short vowels. The second aim was to show that, for these two groups, vowel duration has a different impact on word recognition. A production study showed that Swiss French speakers make a clear duration difference between short and long vowels (the latter are more than twice the length of the former on average) whereas the Parisian French do not. In an identification study which used stimuli pronounced in Swiss French, it was shown that words articulated with long vowels created no recognition problem for Swiss French listeners whereas they did so for Parisian French listeners. These results are discussed in terms of models of speech perception and word recognition.
Nous proposons un nouveau modèle psycholinguistique informatique du français. Le modèle, intitulé FN5, porte sur la reconnaissance de mots parlés, présentés en isolé (déterminant, adjectif antéposé, substantif) ou en suites de deux mots (déterminant et substantif, adjectif antéposé et substantif). Grâce à un lexique de plus de 17 000 mots, à une architecture connexionniste localiste, et à des mécanismes développés spécifiquement (processeur de position, groupements de connexions, et point d'isolation), le modèle peut simuler, de manière séquentielle ou simultanée, certains effets propres au mot isolé (fréquence, longueur, homophonie) ou à une suite de mots (coupure lexicale, enchaînement, liaison, effacement du schwa). De plus, le modèle tient compte de certaines différences qui existent entre le français standard et le français de Suisse romande (nombre de voyelles, durée vocalique en fin de mot, et statut du schwa). Nous décrivons ce modèle, présentons son interface graphique, et l'illustrons avec des exemples de simulation.
Language tools that help people with their writing are now usually
included in today's word
processors. Although these various tools provide increasing support to
native speakers of a
language, they are much less useful to non-native speakers who are
writing in their second
language (e.g. French speakers writing in English). Real errors may
go undetected and
potential errors or non-errors that are flagged by the system may
be taken to be genuine errors
by the non-native speaker. In this paper, we present the prototype of
an English writing tool
which is aimed at helping speakers of French write in English. We first
discuss the kind of
problems non-native speakers have when writing in a second language. We
then explain how
we collected a corpus of errors which we used to build a typology of
errors needed in the
various stages of the project. This is followed by an overview of the
prototype which contains
a number of writing aids (dictionaries, on-line grammar helps, verb
conjugator, etc.) and two
checking tools: a problem word highlighter which lists all the
potentially difficult words that
cannot be dealt with correctly by the system (false friends, confusions,
and a grammar
checker which detects and corrects morphological and syntactic errors.
describe in detail
the automata formalism we use to extract linguistic information, test
and detect and correct errors. Finally, we present a first evaluation of
the correction capacity
of our grammar checker as compared to that of commercially available systems.
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