MétaCan
Menu
Back to cohort
Record W2035647012 · doi:10.1121/1.3508941

An acoustic study of [liquid + stop] sequences by native and second-language speakers of English.

2010· article· en· W2035647012 on OpenAlexaffabout
Terrance M. Nearey, Benjamin V. Tucker

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPerceptContext (archaeology)Variation (astronomy)PerceptionSpeech productionFirst languageVariety (cybernetics)LinguisticsFormantComputer scienceSample (material)AcousticsPsychologySpeech recognitionGeographyArtificial intelligencePhysicsVowel

Abstract

fetched live from OpenAlex

Since the initial work of Mann [Percept. Psychophys. 28, 407–12, (1980)], numerous studies have used liquid + stop clusters in VCCV frames to investigate context effects of liquids on stop perception. However, acoustical studies of related production data have thus far been very limited. The current study will present results of acoustic analyzes of such utterances sampled from 67 native speakers of English (57 from the Canadian Prairie Provinces) and 44 second language speakers of English from a variety of native language backgrounds. The objectives of this work are, first, to understand the nature of variation and covariation of production patterns across a moderate sample of native speakers; second, to investigate how native language background affects production of these sequences (especially those with phonetic realizations of postvocalic liquids that may be quite different from those of English). A key focus will be nature and magnitude of empirical patterns of covariation in relation to perceptual context effects observed in the literature.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.258
Teacher spread0.249 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicSpeech Recognition and SynthesisFrench-language works237,207