Modeling reduced speech using a simulated vocal tract and overlapping acoustic events
Bibliographic record
Abstract
Reduced utterances are characterized by the alteration or deletion of segments (i.e., syllables, words) and often occur in spontaneous or casual speech styles. These reductions are common in everyday communication and are generally understood given sufficient context. However, modeling reduced speech is not trivial, as the effects of context, articulatory limitations, idiosyncrasies, and speech rate need to be considered in order to approximate acoustic observations of reduction. We utilize a speech production model which modulates a simulated vocal tract according to acoustic events organized along a time axis. The present study focuses on validating its capacity for modeling reduced speech. Evidence of its effectiveness is provided by way of synthetic example utterances, each accompanied by a description of the steps taken to synthesize the utterance and acoustic comparison to an unreduced equivalent. Word-medial consonant reductions will be studied first before proceeding to massive reductions where entire syllables and words are affected. We iteratively synthesize reduced utterances until they are evaluated through informal listening to be comparable to real acoustic observations. The results of this study are discussed as validation of the idea that acoustic events can be arranged to model reductions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".