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Record W1974835690 · doi:10.1121/1.4778322

The contribution of auditory temporal processing to the separation of competing speech signals in listeners with normal hearing

2002· article· en· W1974835690 on OpenAlexaff
Trudy J. Adam, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerceptQUIETPerceptionSpeech perceptionActive listeningAuditory scene analysisFidelityJitterSpeech recognitionPsychologyAudiologyStimulus (psychology)Salience (neuroscience)AcousticsComputer scienceCognitive psychologyCommunicationPhysics

Abstract

fetched live from OpenAlex

The hallmark of auditory function in aging adults is difficulty listening in a background of competing talkers, even when hearing sensitivity in quiet is good. Age-related physiological changes may contribute by introducing small timing errors (jitter) to the neural representation of sound, compromising the fidelity of the signal’s fine temporal structure. This may preclude the association of spectral features to form an accurate percept of one complex stimulus, distinct from competing sounds. For simple voiced speech (vowels), the separation of two competing stimuli can be achieved on the basis of their respective harmonic (temporal) structures. Fundamental frequency (F0) differences in competing stimuli facilitate their segregation. This benefit was hypothesized to rely on the adequate temporal representation of the speech signal(s). Auditory aging was simulated via the desynchronization (∼0.25-ms jitter) of the spectral bands of synthesized vowels. The perceptual benefit of F0 difference for the identification of concurrent vowel pairs was examined for intact and jittered vowels in young adults with normal hearing thresholds. Results suggest a role for reduced signal fidelity in the perceptual difficulties encountered in noisy everyday environments by aging listeners. [Work generously supported by the Michael Smith Foundation for Health Research.]

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)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0020.000

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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicNoise Effects and ManagementFrench-language works237,207