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Record W4415781293 · doi:10.1097/aud.0000000000001754

Modeling the Relationship Between Listener Factors and Envelope Fidelity: A Pooled Analysis Spanning a Decade

2025· article· en· W4415781293 on OpenAlexaff
Varsha H. Rallapalli, Jeff Crukley, Emily Lundberg, James M. Kates, Kathryn H. Arehart, Pamela E. Souza

Bibliographic record

VenueEar and Hearing · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsIntelligibility (philosophy)Hearing aidFidelitySpeech perceptionMetric (unit)Envelope (radar)Hearing lossHigh fidelitySpeech processing

Abstract

fetched live from OpenAlex

OBJECTIVES: There is a large variability in speech intelligibility with hearing aids. This variability remains despite the current clinical approaches that provide individualized frequency-specific adjustments to gain in hearing aids. Much of this variability documented in the literature may also be due to differences across studies in terms of outcome measures, test conditions, etc. The objective of this study was to model sources of individual variability in speech intelligibility with hearing aids, in a pooled analysis across four distinct studies that used common methodologies and outcome measures. DESIGN: Deidentified data from 80 unique listeners with bilateral mild to moderately severe sensorineural hearing loss and aged 49 to 92 years were pooled from four published studies. A hierarchical Beta-Binomial (generalized linear mixed-effects) model was implemented to estimate the probability of correct word recognition in the pooled data using a Bayesian framework. Across studies, word recognition was measured for low-context sentences, in multi-talker babble, for a range of signal to noise ratios. Signals were processed through a hearing aid simulator or a wearable device and were customized to the listener's audiogram. Individual studies involved systematic manipulations of wide dynamic range compression, frequency lowering, or microphone directionality. Individual working memory ability was measured using the reading span test. A well-established auditory metric was used to quantify cumulative envelope fidelity (cepstral correlation) from background noise and the hearing aid processing for each listener. RESULTS: The model showed a strong relationship between speech intelligibility and envelope fidelity, confirming previous research findings that higher envelope fidelity was associated with better speech intelligibility. Among the sources of individual variability, working memory had a significant effect on the relationship between speech intelligibility and envelope fidelity. Listeners with higher working memory had significantly better word recognition than those with lower working memory, especially when envelope fidelity was worse. In addition, listeners with lower working memory had better word recognition as envelope fidelity increased. Age and degree of hearing loss (four-frequency pure-tone average) did not have a significant effect on the relationship between speech intelligibility and envelope fidelity. CONCLUSIONS: The analysis of the pooled dataset identified sources of individual variability in aided speech intelligibility, while also overcoming limitations of smaller sample sizes in prior research. The model supported the hypothesis that speech intelligibility is affected by the cumulative envelope fidelity arising from a combination of background noise and hearing aid processing. The study findings indicate that individual variability in speech intelligibility with hearing aid processing is related to working memory after accounting for age and degree of hearing loss. The study highlights the need for individualized treatment of hearing loss beyond the pure tone audiogram. Auditory metrics such as the envelope fidelity metric used in the study may be useful tools in clinical decision-making.

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)0.001
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 score0.039
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.109
GPT teacher head0.340
Teacher spread0.231 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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