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Record W2547853215

A new system of speech privacy criteria in terms of Speech Privacy Class (SPC) values

2010· article· en· W2547853215 on OpenAlexvenueno aff
John S. Bradley, Bradford N. Gover

Bibliographic record

VenueNPARC · 2010
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSpeech recognitionIntelligibility (philosophy)Voice activity detectionNoise (video)Set (abstract data type)Speech processingArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This paper describes a new system` of speech privacy criteria in terms of Speech Privacy Class (SPC) values. SPC values can be used to specify the required speech privacy for new construction or to assess the speech privacy of ex-isting closed rooms. The ASTM E2638 measurement standard defines SPC as the sum of the measured average noise level at the position of a potential eavesdropper outside the room, and the measured average level difference between a source room average and the transmitted levels at the same potential eavesdropper location. For a given combination of level difference and ambient noise level, the likelihood of transmitted speech being audible or intelligible can be related to the probability of higher speech levels occurring in the meeting room, based on the statistics of speech levels from a large number of meetings. For a particular meeting room speech level, there is an SPC value for which transmitted speech would be at the threshold of intelligibility or even at the threshold of audibility. One can create a set of increasing SPC values corresponding to increasing speech privacy and for each SPC value one can give the probability of transmitted speech being either audible or intelligible. This makes it possible to accurately specify speech privacy criteria for meeting rooms and offices, varying from conditions of quite minimal to extremely high speech privacy, with an associated risk of a speech privacy lapse which is acceptable for each situation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.307
Teacher spread0.278 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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