Robust recognition of noisy speech over H.323 networks
Bibliographic record
Abstract
In this paper, we investigate the performance of a speech recognizer on noisy speech transmitted over an H.323 channel, where the minimum mean-square error log spectra amplitude (MMSE-LSA) method is used to reduce the mismatch between training and deployment condition in order to achieve robust speech recognition. In the IP communication environment, one of the sources of distortion to the speech is packet loss. Of course, when ASR systems are used in adverse conditions, their performance degrades. In our work, we not only evaluate the impact of packet losses on speech recognition performance, but also explore the effects of uncorrelated additive noise on the performance. For measuring the influence of missing speech packets on the ASR system performance, we use a Soekris net 4501 IP simulator made by the Engineering Soekris Engineering Company, in order to control packet loss rate. To explore how additive acoustic noise affects the speech recognition performance, six types of noise sources are selected for use in our experiments. The experimental results indicate that the MMSE-LSA enhancement method apparently increased robustness for some type of additive noise under certain packet loss rates over the IP
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".