Normal-rate to fast-rate speech conversion using non-linear compression maps
Bibliographic record
Abstract
This paper presents a new technique to convert normal-rate speech into intelligible fast-rate, speeded speech. Speeded speech has long been recognized for its potential to improve spoken media comprehension; however, current tools to significantly speed playback of non-text media are insufficient due to their reliance on inaccurate phoneme analysis. With the ever increasing amount of non-text media online, a method to speed playback that is agnostic of phonemes is needed. Our technique uses spectral and source components of the acoustics to generate a non-linear compression map that characterizes how conversational-rate speech signals are compressed to achieve analogue fast-rate speech signals. A data set containing conversational- and fast-rate speech pairs was processed to determine compression maps corresponding to each pair. A Recursive Neural Network (RNN) was trained on the set of normal-rate speech and the corresponding compression maps. The RNN was then used to generate compression maps for novel normal-rate speech and ultimately output a fast-rate speech signal. Elicited fast-rate speech and speeded speech conversions technique are now being compared perceptually for intelligibility and naturalness.
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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 | 0.000 |
| 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".