The Effect of Memory Inclusion on Mutual Information Between Speech Frequency Bands
Bibliographic record
Abstract
In this paper, we investigate the effect of temporal correlation on the dependence between the speech narrow and high frequency bands covering the 0.3-3.4 kHz and 3.7-8 kHz ranges, respectively. We follow the technique of using Gaussian mixture modelling of spectral envelopes represented by Mel-frequency cepstral coefficients. The correlation between the disjoint speech frequency bands is quantified through mutual information (MI) and its ratio to highband entropy. Speech exhibits considerable temporal correlation that is not explicitly accounted for by static parametrization of spectral envelopes. Including memory in speech parametrization (through delta features) incorporates such temporal information of speech in its modelling, and hence, MI gains are to be expected resulting in bandwidth extension with better performance. Results show that exploiting delta features can increase certainty about the highband (ratio of MI to highband entropy) by as much as 216% relatively, corresponding to an absolute increase of 12%
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".