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Record W2080536767 · doi:10.1121/1.1849936

Using listening difficulty ratings of conditions for speech communication in rooms

2005· article· en· W2080536767 on OpenAlexaff
Hiroshi Satō, John S. Bradley, Masayuki Morimoto

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsReverberationActive listeningIntelligibility (philosophy)Speech recognitionAcousticsComputer scienceNoise (video)PsychologyAudiologyCommunicationArtificial intelligence

Abstract

fetched live from OpenAlex

The use of listening difficulty ratings of speech communication in rooms is explored because, in common situations, word recognition scores do not discriminate well among conditions that are near to acceptable. In particular, the benefits of early reflections of speech sounds on listening difficulty were investigated and compared to the known benefits to word intelligibility scores. Listening tests were used to assess word intelligibility and perceived listening difficulty of speech in simulated sound fields. The experiments were conducted in three types of sound fields with constant levels of ambient noise: only direct sound, direct sound with early reflections, and direct sound with early reflections and reverberation. The results demonstrate that (1) listening difficulty can better discriminate among these conditions than can word recognition scores; (2) added early reflections increase the effective signal-to-noise ratio equivalent to the added energy in the conditions without reverberation; (3) the benefit of early reflections on difficulty scores is greater than expected from the simple increase in early arriving speech energy with reverberation; (4) word intelligibility tests are most appropriate for conditions with signal-to-noise (S/N) ratios less than 0 dBA, and where S/N is between 0 and 15-dBA S/N, listening difficulty is a more appropriate evaluation tool.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.044
GPT teacher head0.336
Teacher spread0.292 · 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

Citations43
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicHearing Loss and RehabilitationFrench-language works237,207