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Record W4408142328 · doi:10.1016/j.jvoice.2025.02.018

The Influence of the Visual Design of Spaces on Female Speakers’ Vocal Effort: An Exploratory Study

2025· article· en· W4408142328 on OpenAlexaff
Tiffany Chang, Timothy Pommée, Annie Ross, Ingrid Verduyckt

Bibliographic record

VenueJournal of Voice · 2025
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsPolytechnique MontréalUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPsychologyExploratory researchPhonationVoice TrainingLinguisticsAudiologyCognitive psychologyCommunicationSpeech recognitionComputer scienceSociologyMedicine

Abstract

fetched live from OpenAlex

Vocal effort, as defined by Hunter et al, is a subjective perceptual phenomenon influenced by individual and environmental factors. This study investigates the impact of visual room design on vocal effort and measurable vocal demand response, which includes acoustic metrics such as sound pressure level, fundamental frequency ( f o ), and cepstral peak prominence. Twenty female participants performed speech tasks in two acoustically identical rooms that differed visually: an undecorated room (Room A) and a visually stimulating, classroom-like room (Room B). Participants reported significantly lower vocal effort on a visual analog scale in Room A compared with Room B ( P < 0.001), suggesting that visual design can modulate subjective vocal experience. However, acoustic measures of vocal demand response revealed no significant differences between the two environments. These findings reinforce the need to explore multimodal influences on vocal effort, emphasizing the role of visual environments in shaping perceptual experiences of voice use without altering acoustic output. Implications for experimental design and therapeutic interventions 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.177

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 science0.0000.000
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 head0.063
GPT teacher head0.396
Teacher spread0.333 · 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
Published2025
Admission routes1
Has abstractyes

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