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Record W3007699955 · doi:10.1121/10.0000756

The effect of vocal tract parameters on aspiration noise discrimination

2020· article· en· W3007699955 on OpenAlexafffund
Ilse B. Labuschagne, Valter Ciocca

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2020
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsVowelVocal tractNoise (video)AcousticsMathematicsBreathy voiceAudiologyPhonationSpeech recognitionPhysicsComputer scienceMedicine

Abstract

fetched live from OpenAlex

Previous research showed that aspiration noise difference limens in moderately breathy /a/ vowels decreased as the spectral slope of the glottal source spectrum became increasingly steep [Kreiman and Gerratt, J. Acoust. Soc. Am. 131(1), 492-500 (2012)]. The current study investigated whether discrimination of aspiration noise levels was affected by differences in spectral shape due to vowel quality (/æ/ and /i/) and speaker identity (three male speakers) when the slope of the glottal source spectrum was fixed. The results showed that discrimination performance was worse overall for /i/ than /æ/, but the result may have resulted from relatively poor performance for the /i/ vowel of one speaker. Acoustic analyses of the stimuli were performed to estimate the association between acoustic properties and the perceptual outcomes. The results showed that both the smoothed cepstral peak prominence and the harmonic energy level between 2 and 5 kHz may account for the observed differences in aspiration noise discrimination among speakers within each vowel, but not for differences between vowel categories. It is possible that the relationship between the aspiration noise discrimination and aforementioned acoustic properties may be modulated by the spectral distribution of energy across frequency.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.189

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 studies0.0000.000
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 head0.017
GPT teacher head0.277
Teacher spread0.261 · 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 designSimulation or modeling
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

Citations4
Published2020
Admission routes2
Has abstractyes

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