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Record W4206301369 · doi:10.3389/fcomm.2021.807995

Acoustic-Perceptual Factors Both Maintain and Account for the Rarity of the Czech Trill-Fricative

2022· article· en· W4206301369 on OpenAlexafffund
Phil Howson, Philip J. Monahan

Bibliographic record

VenueFrontiers in Communication · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsCzechPerceptionNoise (video)PsychologySpeech recognitionComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Czech has a sibilant inventory that contrasts at three places of articulation: Alveolar, a pre-post-alveolar, and palato-alveolar. The specific aim of this study is to examine the perception of the typologically rare Czech sibilant inventory and to determine whether acoustic-perceptual characteristics play a role in the maintenance of the Czech trill-fricative. These results are compared to a more common three-way sibilant inventory, Polish. Native Czech listeners performed an auditory AX discrimination task in two blocks: A Czech block and a Polish block. Stimuli were embedded in varying levels of noise to increase task difficulty. Signal-to-noise ratio differences affected the perception of the Czech sibilants more than Polish sibilants. Moreover, a multidimensional scaling analysis revealed less perceptual dispersion for the Czech inventory than the Polish inventory. These results suggest that there is greater difficulty maintaining the Czech inventory considering the signal-to-noise comparisons and that this a factor that contributes to its rarity; however, similarities in perceptual dispersion indicate that maintenance across several acoustic-perceptual cues is possible, and Czech shows few signs of losing this typologically rare contrast.

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)0.001
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 score0.352
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.044
GPT teacher head0.334
Teacher spread0.290 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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