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Record W2513722095

Ultrasound overlay videos and their application in Indigenous language learning and revitalization

2016· article· en· W2513722095 on OpenAlexaffvenue
Heather Bliss, Strang Burton, Bryan Gick

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of Health
KeywordsPronunciationComputer scienceIndigenousVisualizationResource (disambiguation)Context (archaeology)MultimediaLinguisticsArtificial intelligenceGeography
DOInot available

Abstract

fetched live from OpenAlex

Pronunciation is one of the major challenges in second language (L2) learning, and in the context of First Nations languages, the challenge can be compounded by both a scarcity of resources (including, in some cases, small numbers of speakers) and the pressures faced by heritage learners to preserve their ancestral language in an authentic way (1). In this paper, we discuss a pronunciation training tool we developed that uses ultrasound visualization technology and its potential application in Indigenous language revitalization. L2 learners rely on auditory and visual information to acquire speech sounds and patterns, and tools like ultrasound that facilitate visualization of the articulatory processes involved in speech production can aid in L2 pronunciation training (2). To make ultrasound visualization accessible and interpretable to a broader audience, we developed a series of ultrasound overlay videos which combine ultrasound images of tongue movement in speech with external profile views of a speaker’s head. There are 91 videos, corresponding to each sound in the International Phonetic Alphabet (see enunciate.arts.ubc.ca for videos and research supporting their effectiveness). While this video library is useful as a general resource, there has also been interest from First Nations groups in customized videos, either to target specific phonological contrasts in a given language or to present a familiar face in a particular linguistic community. Through community partnerships we are developing customized ultrasound overlay videos for four Indigenous languages: Upriver Halq’emeylem, SENCOTEN, Secwepemc, and Blackfoot. We report on these projects, highlighting the potential for ultrasound overlay technology to contribute to revitalization efforts in these and other First Nations languages. (1)   Hinton & Ahlers (1999). The issue of “authenticity” in California language restoration. Anthropology & Education Quarterly, 30 (1), 56-67. (2)   Gick, Bernhardt, Bacsfalvi, Wilson (2008). Ultrasound imaging applications in second language acquisition. Phonology and second language acquisition, Benjamins: 309-22.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.280
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes2
Has abstractyes

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