Accents régionaux en français : perception, analyse et modélisation à partir de grands corpus
Notice bibliographique
Résumé
Large oral corpuses including regional accents of French become today available: their data offer a good base to begin the study of accents. The tools of automatic treatment of the word allow to treat quantities of data more important than the samples that the experts linguists, phoneticians or dialectologues can examine. The French language is spoken in numerous countries worldwide. Our study concerns French of continental Europe, so excluding territories as Quebec, French-speaking Africa or still French overseas departments. We shall study regional accents of France, Belgium and Swiss French. What are the geographical limits inside which it is possible to assert that the speakers have the same accent? The answer to this question is not evident. We adopted the following terminology, adapted to our data: we shall speak about accent when we shall make reference to a precise localization such as a city or a given region; we shall use the term variety to indicate a vaster group. Although numerous studies describe the peculiarities of the accents of French, there are fewer works describing the variation of the language in general, and even less from the point of view of the automatic treatment. Numerous questions remain opened. How many accents can a listener native of French identify? What performances could an automatic system reach for an identical task? Can the indications described in the linguistic literature as characteristics of certain accents be measured in a automatic way? Are they relevant to differentiate varieties of French? Shall we discover the other measurable indications on our corpuses? These indications can be put in connection with the perception? During our thesis, we approached the study of regional varieties of French from the point of view of the human perception as well as of that of the automatic treatment of the word. Traditionally, count of studies in linguistics focus on the study of a precise accent. The automatic treatment of the word allows to envisage the joint study of several varieties of French: we wanted to exploit this possibility. We can so examine what differs from a variety in the other one, what is not possible when a single variety is described. We are lucky to have at our disposal a successful system of automatic alignment of the word. This tool, which allows to segment the sound flow following a phonemic transcription, can show itself precious for the study of the variation. The automatic treatment allows us to consider several styles of word and numerous speakers on quantities of important data with regard to those who were able to be used in linguistic studies led manually. We automatically extracted characteristics of the signal by various methods; we tried to validate our results on two corpuses with accents. The parameters which we held allowed to classify automatically the speakers of our two corpuses.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».