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 (C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> , 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 C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> estimation, 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".