MétaCan
Menu
Back to cohort
Record W2983731978 · doi:10.1080/14992027.2019.1682691

Effects of hearing loss and language proficiency on speech intelligibility over radio transmission with tactical communication devices

2019· article· en· W2983731978 on OpenAlexaff
Christian Giguère, Véronique Vaillancourt, Chantal Laroche

Bibliographic record

VenueInternational Journal of Audiology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAudiologyHearing lossPsychologyIntelligibility (philosophy)MicrophoneSpeech perceptionMedicinePerceptionComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Objective: Study the effects of hearing loss and language proficiency in a speech task over radio transmission.Design: Four TCAPS device conditions (2 models × 2 talk-through modes) were investigated with the Modified Rhyme Test (MRT) conducted in talker-listener pairs immersed in 85-dBA noise. Speech quality ratings and preferred radio volume levels were also collected.Study sample: Thirty-six participants divided into three groups (control, non-fluent, hearing-impaired) took part in the experiment. Participants acted as talkers and listeners when paired with a unique standard individual (fluent and normal hearing) of the same gender.Results: MRT scores were significantly lower in many device conditions when the non-fluent group of participants acted as listeners and talkers and when the hearing-impaired participants acted as listeners, compared to the control group. MRT results were also consistently poorer with one device configured for bone-conducted voice pick-up in the occluded ear compared to another one equipped with an external mouth microphone. Talk-through settings had little effect. MRT results were reflected in the subjective quality ratings. Participants with hearing loss used higher radio volume levels.Conclusions: Language proficiency, hearing loss and method of sensing the talker’s voice are key issues to consider with TCAPS devices.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.178

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 head0.013
GPT teacher head0.320
Teacher spread0.307 · 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 designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of AudiologySame topicHearing Loss and RehabilitationFrench-language works237,207