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Record W2073570336 · doi:10.1121/1.2933329

New Measurement Procedure and Rating Categories for Speech Privacy of Closed Rooms

2008· article· en· W2073570336 on OpenAlexaff
John S. Bradley, Brad N. Gover

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsActive listeningIntelligibility (philosophy)AcousticsComputer scienceSpeech recognitionRoom acousticsNoise (video)ReverberationPsychologyArtificial intelligencePhysicsCommunication

Abstract

fetched live from OpenAlex

A procedure for assessing the speech privacy of enclosed rooms is described based on measured level differences between room-average levels in the room and levels at spot receivers 0.25 m from the outside boundaries of the room. The procedure is not sensitive to the acoustics of the receiving space, assesses conditions at more sensitive listening positions, and can describe variations in speech privacy due to weaker elements such as doors. The arithmetic average of the level differences over speech frequencies from 160 to 5k Hz is used to determine uniform-weighted signal-to-noise ratios found to indicate the audibility and intelligibility of transmitted speech (J. Acoust. Soc. Am. 116 (6) 3480-3490 (2004)). The degree of speech privacy is related to the speech levels in the room, the measured transmission characteristics and ambient noise levels outside the room. The probability of transmitted speech being audible or intelligible to eavesdroppers is related to the likelihood of louder speech in the meeting room. The measured statistics of speech levels in a large number of meeting rooms have been used to describe categories of speech privacy of rooms ranging from minimal privacy to situations where transmitted speech would be very rarely audible.

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: Methods · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.297

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 studies0.0000.000
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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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