Determination of Room Impulse Response for synthetic data acquisition and ASR testing
Bibliographic record
Abstract
Automatic Speech Recognition (ASR) works best when the speech signal best matches the ones used for training. Training, however, may require thousands of hours of speech, and it is impractical to directly acquire them in a realistic scenario. Some improvement can be obtained by incorporating typical building acoustics measurement parameters such as RT, Cx, LF, etc., with limited gain. Instead, we estimate Room Impulse Responses (RIRs), and convolve speech and noise signals with the estimated RIRs. This produces realistic signals, which can then be processed by the audio pipeline, and used for ASR training. In our research, we use rooms with variable acoustics and repeatable source-receiver positions. The receivers are microphone arrays making the relative phase and magnitude critical. A standard mouth simulator for voice signals at various positions in the room is under robot control. A limited corpus of speech data as well as noise sources is recorded and the RIR at these 27 positions is determined using a variety of methods (chirp, MLS, impulse, and noise). The convolved RIR with the “clean speech” is compared to the actual measurements. Test methods used, differences from the measurements, and the difficulty of determining the unique RIR will be presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".