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Record W4394089478 · doi:10.6084/m9.figshare.24147242

An Exploration of Voice Quality in Mothers Speaking Canadian English to Infants

2023· dataset· en· W4394089478 on OpenAlexaboutno aff
Andrew Cheng, Elise McClay, H. Henny Yeung

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)PsychologyS VoiceDevelopmental psychologyCommunicationComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Research on the acoustic characteristics of Infant Directed Speech (IDS) in North American English indicates that it is generally higher-pitched than Adult Directed Speech (ADS) and has unique prosodic characteristics, which is commonly found across many spoken languages. However, very little research has addressed another important aspect of prosody: voice quality. In the current study, 25 English-speaking mothers from Canada were recorded speaking to their infant children and to an adult peer. Five acoustic measures of voice quality, including glottal constriction, spectral tilt, Harmonic-to-Noise Ratio (HNR), and Cepstral Peak Prominence (CPP), were analyzed. Only CPP, a measure of the breathiness of a speaker’s voice, and corrected H1-A2, a measure of vocal creakiness, were found to be significantly different between the IDS and ADS registers. Sociolinguistic research identifies voice quality as a key indicator of speech style and persona; we connect the pattern of breathiness in IDS to a possible “parental persona” that builds on the affective intent of IDS (rather than the pedagogical intent), with suggestions for future research.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.117
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.217
GPT teacher head0.450
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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