Objective and subjective speech quality evaluation of wideband noise reduction algorithms.
Bibliographic record
Abstract
Subjective and/or objective measurements of speech quality are important in benchmarking speech enhancement algorithms. Subjective measures include ratings of speech quality by listeners, whereas objective measures compute a metric based on the clean and enhanced speech samples. While subjective quality ratings are the “gold-standard,” they are also time- and resource-consuming. An objective metric that correlates highly with subjective data is attractive, as it can act as a substitute for benchmarking and fine-tuning the noise reduction algorithms. In this paper, the performance of several noise reduction algorithms for wideband (50–7000 Hz) telephony application was evaluated both subjectively and objectively. A custom wideband noise reduction database was created that contained speech samples corrupted by different background noises at different signal to noise ratios and processed by seven different noise reduction algorithms. Speech samples in the database were subsequently rated by a group of 32 listeners with normal hearing capabilities. Several objective metrics including log-likelihood ratio, weighted spectral slope, PESQ, and the loudness pattern distortion (LPD) measure based on the Moore–Glasberg auditory model were used to predict the subjective ratings. Results showed that the subjective ratings were highly reliable and the LPD metric correlated the best with subjective ratings of enhanced wideband speech.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".