Seeing Speech: A Pronunciation Toolkit for Indigenous Language Teaching and Learning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".