Adaptive Time-Scale Modification for Improving Speech Intelligibility Based On Phoneme Clustering For Streaming Services
Bibliographic record
Abstract
Time-scale modification (TSM) is important in streaming services, including over-the-top (OTT) platforms, audiobooks, and online lectures. Although TSM modifies the speed of audio while maintaining other audio attributes such as the pitch and timbre of the speaker, it unnaturally distorts audio signals and makes spoken content difficult to understand. This study proposes an adaptive time-scale modification algorithm (ATSM); that adaptively varies the speaking rate for each phoneme cluster of speech to improve speech intelligibility. The proposed algorithm performs forced alignment using Montreal forced aligner and time-scale reconstruction using an adaptive speaking rate based on dynamic time warping. To validate the proposed algorithm, the diagnostic rhyme test (DRT) score, comparison mean opinion score (CMOS), and fast dynamic time warping (FastDTW) score of ATSM are compared with those of conventional TSMs. The results show that the speech compressed with the proposed algorithm has improved speech intelligibility than that of speech compressed with other algorithms.
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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 | 0.000 | 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".