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Life-span changes in spoken French: a study on 400 speakers from 20 to 90 years old

2017· article· en· W7133365641 sur OpenAlexaboutno aff
Véronique Delvaux, Cécile Fougeron, Lucie Ménard, Marina Laganaro

Notice bibliographique

RevueORBi UMONS · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueVoice and Speech Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFormantSpeech productionVocal tractArticulation (sociology)Voice-onset timeManner of articulationVariation (astronomy)VoiceVariety (cybernetics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Our knowledge of life-span changes in the speech of adults is quite sparse. Documented in a variety of studies and age-group comparisons, age-related changes have been found in voice quality parameters (Ramig & Ringel, 1983; Russell et al., 1995; Ferrand, 2002; Xue & Hao, 2003), pitch (with an increased in mean f0 for older males but not older females, Harnsberger et al., 2008), and in some instances, formant frequencies and VOT (Xue & Hao, 2003; Torre & Barlow, 2009, but see Fletcher et al. 2015). Overall, the more robust (and documented) age-related differences in speech production concern speech rate (mainly, articulation rate), with older speakers speaking more slowly than younger adults (Verhoeven et al., 2004; Jacewicz et al., 2009; Quené, 2008, Staiger et al. 2017). A better understanding of the evolution of speech throughout adulthood is critical for clinical research where data have to be age-standardized. It is also crucial for our general understanding of the complexity of the speech production system since age-related changes can as well originate from structural changes at the peripheral level (anatomical and physiological changes in the speech apparatus affecting pulmonary function, laryngeal structure and/or vocal tract length), or from neurological changes affecting speech motor control or cognitive functions (e.g. Linville, 2001; Torre & Barlow, 2009; Seidler et al. 2010). The study we present here aims at further documenting life-span changes over adulthood by looking at cross-sectional data covering a large range of between-speaker and within-speaker variation in speech productions. This is done through an on-going investigation of the recently collected 'monpAGE' speech database. The 'monpAGE' database is an outcome of the MonPaGe project whose general goal is the development of a speech screening protocol directed for French-speaking patients presenting signs of speech motor disorders. For the validation of the protocol, a set of reference values have been collected from 400 French-speaking adults, half male and half female, encompassing five age groups with 80 speakers per groups: [18-39], [40-49], [50-59], [60-75], [75+]. Speakers have been recorded in Paris, Geneva, Mons and Montreal in order to cover several regional varieties. These cross-sectional data thus form one of the largest French database available for observing normal changes in the speech of adults as a function of gender and age. A variety of linguistic material produced in different speech conditions is included for each speaker in order to assess multiple speech dimensions. Tasks include repetition; reading; automated production of the days of the week; diadochokinetic tasks (DDK, fast and precise repetitions of alternating speech movements); semi-spontaneous production in a picture description task. The speech material is aimed at assessing articulatory precision, coarticulation, expressive and linguistic prosody, speech and articulation rate, voice quality, maximum phonation time. Phonetico-phonological complexity factors (structural patterning, length, planning demands) are also manipulated. The analysis of this large database is currently in progress and further data will be presented at the congress. To date, the analysis of a subset of 100 speakers covering 6 age groups [18-29], [30-39], [40-49], [50-59], [60-75], [75+]) has provided interesting results concerning age-related changes in coarticulatory patterns as well as in the temporal organization of speech as a function of speech task. No effect of age (age_group) is found in maximum phonation time suggesting preserved pneumo-phonatory control over time despite potential reduction in pulmonary function. In contrast, a significant effect of age is observed in speech rate in tasks requiring the articulation of sound sequences, with a consistent drop in articulation rate for the last two age groups. Interestingly, this effect of age is found both in the sentence reading task and in the DDK task. In this maximum performance task, no interaction is found between age and the complexity of the sequence to be repeated (CVCVCV or CCVCCVCCV). This uniform decrease in articulation rate thus suggests a general decline in the temporal organization of speech, independent of the motoric and cognitive complexity of the task (as found in German by Steiger et al., 2016). Regarding coarticulation, anticipatory effects have been measured in local and non-local environments. Local V-to-C coarticulation is tested on the spectral caracteristics of /s/ according to the rounding of the following vowel (/tesi/ vs. /tesy/). Non-local V-to-V coarticulation is tested on the spectral properties of V1/a/ according to the height of V2/a,i/ (/maba/-/mabi/, /laspa/-/laspi/). Differences in coarticulation size are found across age groups in both local V-to-C and non-local V-to-V coarticulation, but as it is, these differences result from specific age groups idiosynchrasies and do not reveal an age-related trend. Further analysis on this point will be provided at the conference based on the full dataset (including more speakers per age group), as well as complementary measures documenting other speech dimensions (articulatory precision of vowels and consonants, voice quality, prosody, pausing...).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,048
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,044
Tête enseignante GPT0,313
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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