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Record W2973951857 · doi:10.1037/xap0000247

The benefit to speech intelligibility of hearing a familiar voice.

2019· article· en· W2973951857 on OpenAlexfundno aff
Ysabel Domingo, Emma Holmes, Ingrid S. Johnsrude

Bibliographic record

VenueJournal of Experimental Psychology Applied · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiscourse Analysis and Cultural Communication
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologySentenceIntelligibility (philosophy)PsycINFOSpeech perceptionAudiologyQUIETCognitive psychologyLinguisticsPerceptionMEDLINE

Abstract

fetched live from OpenAlex

benefits, respectively). To better understand how familiarity improves intelligibility, we measured the familiar-target and familiar-masker benefits in older and younger spouses using a more challenging matrix task, and compared the benefits listeners gain from spouses' and friends' voices. On each trial, participants heard two sentences from the Boston University Gerald (Kidd, Best, & Mason, 2008) corpus (" ") and reported words from the sentence beginning with a target name word. A familiar-masker benefit was not observed, but all groups showed a robust familiar-target benefit and its magnitude did not differ between spouses and friends. The familiar-target benefit was not influenced by relationship length (in the range of 1.5-52 years). Together, these results imply that the familiar-target benefit can develop from various types of relationships and has already reached a plateau around 1.5 years after meeting a new friend. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.417
Teacher spread0.373 · 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 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

Citations33
Published2019
Admission routes1
Has abstractyes

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