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Record W4226382894 · doi:10.1075/bct.121.02mun

Foreign accent, comprehensibility and intelligibility, redux

2022· book-chapter· en· W4226382894 on OpenAlexaff
Murray J. Munro, Tracey M. Derwing

Bibliographic record

VenueBenjamins current topics · 2022
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsIntelligibility (philosophy)PronunciationLinguisticsStress (linguistics)Computer sciencePsychologyReduxNatural language processingSpeech recognitionPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract We revisit Munro and Derwing (1995a) , providing retrospective commentary on our original methods and findings. Using what are now well-established assessment techniques, the study examined the interrelationships among accentedness, comprehensibility, and intelligibility in the speech of second-language learners. The key finding was that the dimensions at issue are related, but partially independent. Of particular note was our observation that speech can be heavily accented but highly intelligible. To provide a fresh perspective on the original data we report a few new analyses, including more up-to-date statistical modeling. Throughout the original text we intersperse insights we have gained since the appearance of the 1995 paper. We conclude with retrospective interpretations, including thoughts on the relevance of the study to contemporary second language teaching and especially pronunciation instruction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.142
GPT teacher head0.395
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2022
Admission routes1
Has abstractyes

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