MétaCan
Menu
Back to cohort
Record W2145910889 · doi:10.1109/tbme.2008.2007910

Automatic Modeling of Acoustic Perception of Breathiness in Pathological Voices

2008· article· en· W2145910889 on OpenAlexaff
Eduardo Castillo-Guerra, Adel Ruiz

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2008
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBreathy voiceSpeech recognitionPerceptionComputer scienceVowelMeasure (data warehouse)AcousticsPsychologyPhonationAudiologyData miningPhysicsMedicine

Abstract

fetched live from OpenAlex

This paper revisits the modeling of acoustic perceptions of breathy voice (BV) quality for automatic assessment of perturbations in pathologic speech. Several acoustic measures related with the signal periodicity, harmonic components, and aspiration noise are studied to predict breathiness judgments performed on sustained vowel phonations. A novel comprehensive automatic measure is proposed that provides the highest correlation index (88.5%) with breathiness judgment performed by trained specialists on simulated and recorded utterances. The new measure reveals the most relevant aspects of BV quality and provides a vehicle to obtain reliable objectives judgments of such speech perturbation.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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

Citations22
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Biomedical EngineeringSame topicVoice and Speech DisordersFrench-language works237,207