Bibliographic record
Abstract
It is well known that automatic speech recognition (ASR) requires good spectral analysis in order to have successful ASR accuracy. A wideband spectrogram seems to contain all the needed acoustic information to map any given speech signal into its corresponding sequence of phonemes. (For ASR, language models are often used to augment acoustics, but here we will limit ourselves to acoustic analysis.) Various methods beyond the basic Fourier transform have found success in ASR, e.g., linear predictive analysis, wavelets, and mel-frequency cepstra (MFCC). These have all been focussed on extracting an efficient set of spectral parameters to facilitate phonetic discrimination. Part of the difficulty is separating spectral envelope information from excitation parameters, as variations in pitch are largely viewed as orthogonal to phoneme recognition. Another complicating factor is that amplitude and frequency scales in speech production and perception are better modeled as nonlinear (unlike the linear, fixed-bandwidth approach of Fourier transforms). Modern ASR techniques are far from optimal, as the front-end data compression yielding MFCCs, for a basic 80-ms phoneme, typically has more than 100 parameters, to distinguish among approximately 32 phonemes (a 5-bit choice). We will investigate various ways to render ASR analysis more efficient. [Work supported by NSERC-Canada.]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".