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Record W7054466816

Against listener-oriented sub-phonemic differentiation

2022· other· en· W7054466816 on OpenAlexaboutno aff

Bibliographic record

VenueCLOK (University of Central Lancashire) · 2022
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsFilter (signal processing)NucleofectionFrame (networking)Limiting
DOInot available

Abstract

fetched live from OpenAlex

The English word "like" has attracted much sociolinguistic interest, but also phonetic and phonological research: Due to its many functions (Podlubny et al. 2015 report 11 in Canadian English, but we argue there may be as many as 16), it is a prime target for studies of sub-phonemic differences between near-homophones. For instance, Drager (2009) famously showed differences in segment length or realisation for different functions of "like" in New Zealand English; Podlubny et al. (2015) similarly found vowel realisation and length differences between "like" functions in Canadian English. However, these differences are small, when they are found at all – Schleef and Turton (2018) do find different vowel realisations between "like" functions in Edinburgh and London varieties of English, and argue that these are due only to prosodic contexts for certain functions favouring reduction. This possibility casts doubt on how systematic and thus how transmittable/learnable such differences would be. Therefore, we study "like" in another regional accent (or pan-regional standard, following Strycharczuk et al. 2020) in England to see if differences exist there; the questions of origin and transmission routes would, of course, be for future research. \nWe recorded 11 young adult (age range: 18 to 25 years) speakers of English from the North-West of England in informal conversation with a family member or friend (as in e.g. Warner and Tucker 2011), and also reading a list of 36 sentences containing different functions of like. The conversations were transcribed manually; transcripts and sentence-lists were force-aligned to recordings using the self-training Montreal Forced Aligner (McAuliffe et al. 2019). We extracted all "like" tokens and calculated/annotated segmental and word-level features (namely the duration of every token and segment, average speech rate in a window extending up to 3 words either side of the token, F1 and F2 at 25% and 75% through each vowel segment, and the Euclidean distance between these formant values as a measure of diphthongisation) as well as context features (Beckman and Hirschberg 1994's ToBI break index strength following the token, position of the "like" token in the utterance, and the segments and words immediately preceding and following the token). To account for predictability effects on pronunciation (e.g. Hall et al. 2018), we extracted the bigram frequencies either side from SUBTLEX (van Heuven et al. 2014). We used mixed-effects regression models and agglomerative hierarchical clustering to investigate this data for any systematic differences. \nCounter to prior research, we find no systematic acoustic differences between "like"s of different functions: Four separate regression models (with the word length, /k/ segment length, ratio of /l/ segment length to vowel segment length, and the diphthongisation measure as dependent variables respectively) as well as hierarchical clustering all fail to show any reliable difference in like realisation by "like" function. The only strong acoustic differences we find are between male and female speakers (in pitch and formants) as well as between conversation and sentence-list tokens (longer tokens and more diphthongal vowels in sentence-list reading). \nThe sex and genre differences are unsurprising, but serve as sanity checks. The fact that we found no other reliable differences in like realisation by function shows that the North-West England accent does not differentiate between functions of "like" phonetically, despite how useful this would be given the number of functions. This, we argue, suggests that listener-oriented accounts of different mental representations for (near-)homophones are not borne out.

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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)0.013
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0050.007
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.006

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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 designTheoretical or conceptual
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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