Non-Intrusive Signal Analysis for Room Adaptation of ASR Models
Bibliographic record
Abstract
We present a new deep-learning-based non-intrusive signal assessment method (NISA+) that performs a joint estimation of a large set of speech signal parameters, including those related to reverberation (C50, DRR, reflection coefficient and room volume), background noise (SNR), perceptual speech quality (PESQ), speech intelligibility (ESTOI), voice activity detection, and speech coding (codec presence and bitrate). We show that neural embedding based combination of spectral features with an LSTM and modulation features with a convolution neural network enable NISA+ to achieve state of the art performance. Particularly, for non-intrusive PESQ and C50estimation, we show around 15% relative reduction in estimation error compared to our previous best results. We also show that NISA+ can be used to perform targeted data augmentation for generating training data for ASR that matches the signal characteristics extracted from a small sample of data recorded in a target room acoustic environment. We show that a 9.6% word error rate reduction can be achieved relative to an ASR model trained with random augmentation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".