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Record W2091959149 · doi:10.1504/ijvnv.2012.051538

The evaluation of speech intelligibility in a simulated driving environment using the hearing in noise test

2012· article· en· W2091959149 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Vehicle Noise and Vibration · 2012
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIntelligibility (philosophy)Active listeningComputer scienceSentenceSpeech recognitionAcousticsPerceptionSpeech perceptionEngineeringPsychologyArtificial intelligenceCommunication

Abstract

fetched live from OpenAlex

The implementation of the hearing in noise test (HINT) was carried out using an NVH driving simulator in order to evaluate the speech intelligibility in a car between driver and passenger for a variety of driving speeds and the configurations of the talker and of the listener. The sentence reception threshold (sSRT) was determined for each of the various communication situations. When presented with the same listening task, the participants required on average an approximate 3 dB increase in sound pressure level of the HINT speech material while driving and listening compared to when just listening, for an equivalent speech intelligibility performance. A suggested improvement to the current state of art would be to develop a driving simulation for acoustic perception jury testing as described in this study. This would form the basis of a standard method for a more complete, accurate and repeatable evaluation of in-vehicle speech intelligibility.

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.

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)0.002
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 score0.258
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.305
Teacher spread0.270 · 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