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Record W7054466816

Against listener-oriented sub-phonemic differentiation

2022· other· en· W7054466816 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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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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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.565
Threshold uncertainty score0.979

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)0.5650.000

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 head0.006
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