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Record W2098171489 · doi:10.1109/icassp.2004.1325973

A study of design compromises for speech coders in packet networks

2004· article· en· W2098171489 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 institutionsVoiceAge (Canada)Université de Sherbrooke
Fundersnot available
KeywordsComputer scienceEncoderRobustness (evolution)Lossy compressionNetwork packetSpeech recognitionRedundancy (engineering)Speech codingBit rateReal-time computingComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

We present an objective and subjective comparison of alternative methods for improving the robustness of speech coders in packet networks. The two approaches are considered: 1) adding redundancy in the packets to improve the robustness of a baseline encoder; 2) reducing (or eliminating) inter-frame dependencies at the encoder. It is shown that both approaches have to trade bit rate and/or delay for quality over lossy channels. Formal subjective tests clearly show that, using relatively simple forward error correction methods, standard coders such as ITU-T recommendation G.729 can be made significantly more robust than "frame-independent" coders, at a lower or similar bit rate.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.478
Threshold uncertainty score0.366

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 communication0.0000.000
Open science0.0010.000
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 head0.061
GPT teacher head0.324
Teacher spread0.263 · 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

Citations26
Published2004
Admission routes1
Has abstractyes

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