MétaCan
Menu
Back to cohort
Record W1594640825 · doi:10.1109/icassp.1995.479637

Spectral excitation coding of speech at 2.4 kb/s

2002· article· en· W1594640825 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 institutionsSimon Fraser University
Fundersnot available
KeywordsSpeech codingQuantization (signal processing)Vector quantizationHarmonic Vector Excitation CodingCodec2ExcitationSpeech recognitionDimension (graph theory)Linear predictive codingCoding (social sciences)Computer scienceAlgorithmAdaptive Multi-Rate audio codecCodecAcousticsMathematicsPhysicsSpeech processingVoice activity detectionTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

We present spectral excitation coding (SEC), a speech codec based on a sinusoidal model applied to the excitation signal. A phase dispersion algorithm allows the same model to be used for voiced as well as unvoiced and transitional sounds. The phase dispersion algorithm significantly improves the perceived quality resulting in more natural reconstructed speech. A new technique for variable dimension vector quantization called nonsquare transform vector quantization (NSTVQ) is used for quantization of the harmonic magnitudes. The SEC system at 2.45 kb/s achieved an MOS score 0.8 points higher than the 2.4 kb/s ZPC-10 standard. A preliminary 1.85 kb/s SEC system which uses zero-bit magnitude quantization is also presented. Informal listening tests indicate that the quality of the 1.85 kb/s system exceeds that of the LPC-10 standard.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.700
Threshold uncertainty score0.422

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 science0.0010.000
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 head0.035
GPT teacher head0.275
Teacher spread0.240 · 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

Citations16
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Data Compression TechniquesFrench-language works237,207