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Record W1601379362 · doi:10.1109/acssc.1994.471637

Speech coding for wireless communication

2002· article· en· W1601379362 on OpenAlexaff
P. Mermelstein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGSMComputer scienceWirelessSpeech codingQuality of serviceWirelineCoding (social sciences)Computer networkTelecommunicationsVoice activity detectionTransmission (telecommunications)Speech recognitionSpeech processingMathematics

Abstract

fetched live from OpenAlex

Personal communication systems will be judged primarily on the quality of speech communication they provide. The voice-call capacity depends directly on the bit-rate required to achieve the quality objectives of the specific service. To provide speech quality on wireless systems that is comparable to that attained on today's wireline systems, at least 16 kb/s are required. New algorithms are being considered for a standard to transmit speech at 8 kb/s in the presence of modest transmission errors. Mobile systems with slightly lower quality requirements in service today employ 8 kb/s (IS-54 and IS-95) and 13 kb/s (GSM), respectively. Efforts are under way to halve these values and thereby double the capacity. The paper reviews current research into achieving higher speech quality at lower bit rates under a variety of environmental and transmission conditions. It provides a short-range perspective on where significant progress is expected in the short-term.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0330.023

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 head0.049
GPT teacher head0.298
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2002
Admission routes1
Has abstractyes

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