Acoustic Analysis for Automatic Speech Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a pattern recognition application, automatic speech recognition (ASR) requires the extraction of useful features from its input signal, speech. To help determine relevance, human speech production and acoustic aspects of speech perception are reviewed, to identify acoustic elements likely to be most important for ASR. Common methods of estimating useful aspects of speech spectral envelopes are reviewed, from the point of view of efficiency and reliability in mismatched conditions. Because many speech inputs for ASR have noise and channel degradations, ways to improve robustness in speech parameterization are analyzed. While the main focus in ASR is to obtain spectral envelope measures, human speech communication efficiently exploits the manipulation of one's vocal-cord vibration rate [fundamental frequency (F0)], and so F0 extraction and its integration into ASR are also reviewed. For the acoustic analysis reviewed here for ASR, this work presents modern methods as well as future perspectives on important aspects of speech information processing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it