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Record W4247535256 · doi:10.24908/iqurcp.8898

Accent Identification in RP, CE, and GA By Native and Non-Native English Speakers

2018· article· en· W4247535256 on OpenAlexvenueaboutno aff
Jonathan P. Reid

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsPronunciationStress (linguistics)American EnglishLinguisticsActive listeningPsychologyVarieties of EnglishRaising (metalworking)Degree (music)CommunicationMathematics

Abstract

fetched live from OpenAlex

This study was conducted in order to determine various groups’ accuracy in identifying three major standard English accents. The main purpose of the experiment that was performed was to determine how well speakers familiar with these accents could tell them apart from other accents. It focused on comparing the test subjects’ recognition of Canadian English pronunciation with General American pronunciations. Received Pronunciation was used as a control as it is generally considered to differ much more from the standard North American varieties than they differ from one another.Within North America, the 'standard' accents of Canada and the US are quite similar. So similar, that one of the experiment’s hypotheses is that that despite being identified by linguists as different, many native speakers of the two dialects would have difficulty telling the difference themselves. The differences in the features of Canadian English (CE) and General American (GA) have been identified and studied by linguists before, but what this experiment sought to determine was the degree to which speakers of these dialects could tell them apart purely through listening.Canada is given some degree of stigma from the United States for its dialect, and has had fun poked at it for such Canadianisms as the use of 'eh?' and Canadian Raising- Americans will exaggerate the difference when illustrating it, saying “aboot” [ə'but] for about [əˈbʌʊt]. But how well can they perceive the difference when not already informed about the speaker's origins?An online survey was prepared, with audio clips or words in isolation and sentences, spoken by speakers of GA, CE, and RP, specifically using words that exhibited features that vary between the accents. This allowed us to examine subjects’ degree of recognition with, and without prosody, and to analyze the degree to which prosody affects accent recognition. In order to better determine how prior exposure influences accent recognition, the subjects were broken down into three groups: Native speakers of Canadian English, native speakers of American English and ESL speakers who had had prior exposure to Canadian English.One of the main findings of this experiment is that more than 80% of American respondents recognized their own national standard accent, and around 67% recognized the Canadian accent; while only 62% of the Canadian respondents recognized the Canadian accent accurately. Compared to Canadians, Americans were better at telling the North American varieties of spoken English apart.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.084
GPT teacher head0.408
Teacher spread0.324 · 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 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

Citations1
Published2018
Admission routes2
Has abstractyes

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