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Record W1824492532 · doi:10.1109/scft.1993.762346

Speech coding over frame relay networks

2005· article· en· W1824492532 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceFrame (networking)Code-excited linear predictionFrame RelaySpeech codingRelayInter frameSpeech recognitionLinear predictive codingCoding (social sciences)Voice activity detectionReal-time computingSpeech processingNetwork packetReference frameComputer networkPower (physics)Mathematics

Abstract

fetched live from OpenAlex

In this paper, the problem of transmitting compressed voice over frame relay networks is addressed. To overcome variable frame delay and frame loss in the networks, an adaptive build out delay mechanism combining silence detection and time-scale modification is described for use in an embedded 8/16 kb/s CELP-MPE speech coder. Vector Linear Prediction (VLP) is then discussed for the purpose of voice frame re-construction. During frame losses, an adaptive energy control mechanism is introduced to suppress sudden energy explosion in the reconstructed frame. Objective and subjective results at random frame error rates are obtained and it is shown that there is virtually no degradation in perceptual quality when the frame loss rate is as high as 1%.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.699
Threshold uncertainty score0.377

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 communication0.0000.001
Open science0.0010.001
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 head0.012
GPT teacher head0.278
Teacher spread0.266 · 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

Quick stats

Citations10
Published2005
Admission routes1
Has abstractyes

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