Seeing Speech: A Pronunciation Toolkit for Indigenous Language Teaching and Learning
Notice bibliographique
Résumé
Pronunciation can present a serious challenge for language teachers and learners (e.g., Munro & Derwing 2015). In the context of Indigenous languages in particular, this can be compounded by a number of factors, including small numbers of speakers and teachers, a paucity of pedagogical resources and clear descriptions of sound systems, and the pressures faced by heritage learners to authentically preserve their ancestral language (Carpenter 1997; Hinton 2011; Hinton & Ahlers 1999). Latent speakers may be inhibited from speaking by perceived concerns over their pronunciation, particularly in the presence of elders (Basham & Fatham 2008), and other learners may face similar social and linguistic challenges. Despite these hurdles, pronunciation is considered by many to be an important aspect of Indigenous language learning, and one which requires creative community-oriented solutions (AUTHOR & Kell 2015; Carpenter 1997). Towards this end, we have developed a pronunciationlearning toolthat incorporates ultrasound technology, giving learners a visual aid to help them learn to articulate challenging or unfamiliar sounds, for example “back of the mouth” consonants (e.g. /k/ vs. /q/). Ultrasound is used to create videos of a model speaker’s tongue movements during speech, which are then overlaid on videos of an external profile view of the model’s head to create ultrasound-enhanced pronunciation videos for individual words or sounds (Abel et al. 2015). A key advantage of these videos is that they allow learners direct access to the articulatory shapes and movements that are involved in pronouncing challenging words or sounds; learners are able see how speech is produced rather than just hear and try to mimic it. Although ultrasound-enhanced videos were originally developed for commonly taught languages such as Japanese and French, there has been widespread interest from Indigenous communities in Western Canada to develop their own customized videos. To date, we have partnered with communities in Alberta and British Columbia to develop videos for four languages: SENĆOŦEN, Secwepemc, Halq’emeylem, and Blackfoot. Community-driven and capacity-building, these projects involved training community members in how to produce customized ultrasound-enhanced videos using our toolkit. The resulting videos will be featured in our presentation, along with demonstrations of how and why to use ultrasound in pronunciation teaching. Our goal is to show that the ultrasound-enhanced videos can help to address some of the challenges of pronunciation learning in Indigenous languages by giving learners a new way to understand pronunciation that focuses on seeing speech. References Abel, J., B. Allen, S. Burton, M. Kazama, M. Noguchi, A. Tsuda, N. Yamane, & AUTHOR. 2015. Ultrasound-Enhanced Multimodal Approaches to Pronunciation Teaching and Learning. Canadian Acoustics 43 (3), 130-131. Basham, C. and A. Fathman. 2008. The latent speaker: Attaining adult fluency in an endangered language. International Journal of Bilingual Education and Bilingualism, 11: 577-97. AUTHOR and S. Kell. Pronunciation in the context of language revitalization. Paper presented at ICLDC 4, 2015. Carpenter, V. 1997. Teaching Children to "Unlearn" the Sounds of English. In Teaching Indigenous Languages, ed. by Jon Reyhner. Flagstaff, AZ: Northern Arizona University, pp. 31-39. Hinton, L. 2011. Language revitalization and language pedagogy: New teaching and learning strategies. Language and Education 25(4): 307-318, Hinton, L. and J. Ahlers. 1999. The issue of “authenticity” in California language restoration. Anthropology & Education Quarterly, 30: 56-67. Munro, M. J. & Derwing, T. M. 2015. A prospectus for pronunciation research in the 21st century: A point of view. Journal of Second Language Pronunciation 1(1): 11-42.
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 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 | 0,000 | 0,000 |
| 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 | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».