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Record W2624142157 · doi:10.1121/1.4988066

Reading aloud: Acoustic differences between prose and poetry

2017· article· en· W2624142157 on OpenAlexaff
Filip Nenadić, Benjamin V. Tucker

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPoetryRead aloudLinguisticsReading (process)UtteranceReading aloudStanzaSerbianLiteratureArtComputer sciencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

Research on silent reading has shown that text genre influences the way texts are read, including differences between prose and poetry (e.g., Zwaan, 1994; Hanauer, 1998). There is little data examining whether text layout (prose vs. poetry) affects the way it is read aloud by non-expert readers, and, if yes, how do readers express those differences acoustically. Native speakers of Serbian (N = 28) and English (N = 37) read aloud twenty short texts in their native language. Stimuli were original texts that were acceptable as both prose and poetry, written by young published authors. Each text was formatted in four layouts (prose left aligned and justified, a single stanza and verses in multiple stanzas). Each participant saw each text in only one of these layouts. Separate mixed effects logistic regression analyses were performed for each language, testing whether prose vs. poetry layouts influenced the silent period and utterance duration, pitch, and intensity values of the productions. Differences and similarities between reading prose and poetry and between Serbian and English participants are discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.671

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.024
GPT teacher head0.273
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 designOther design
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
Published2017
Admission routes1
Has abstractyes

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