Description of GSM enhanced full rate speech codec
Bibliographic record
Abstract
This paper describes the enhanced full rate (EFR) speech codec that has been standardized by ETSI for the GSM mobile communications system in 1996. The codec was developed jointly by Nokia and the University of Sherbrooke. It operates at 12.2 kbit/s speech coding (source coding) bit-rate and provides speech quality equivalent to that of wireline telephony (G.726 32 kbit/s ADPCM). The algorithm is based on the algebraic code-excited linear prediction (ACELP) technology, using 20 ms speech frames. The GSM EFR speech codec provides substantial quality improvement compared to the current GSM full rate (FR) and half rate (HR) codecs. The old GSM codecs lag far behind wireline quality even in error-free conditions, while the EFR codec provides wireline quality also for the most typical error conditions. With the EFR codec, wireline quality is also sustained in the presence of background noise and in tandem connections (mobile-to-mobile calls). The codec was defined using fixed-point basic operators with complexity estimated at 18 WMOPS (below that of the GSM half-rate codec).
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".