The Effect of Pitch Peak Alignment on Sentence Type Identification in Russian
Bibliographic record
Abstract
This paper reports the results of an experimental phonetic study examining pitch peak alignment in production and perception of three-syllable one-word sentences with phonetic rising-falling pitch movement by speakers of Russian. The first part of the study (Experiment 1) utilizes 22 one-word three-syllable utterances read by five female speakers of Russian as a declarative, an exclamation, and an interrogative. Significant differences in the alignment of pitch peak across declaratives and exclamations on the one hand and interrogatives on the other hand are observed. The second experiment tests whether these pitch peak alignment differences are employed in speech perception. Experiment 2 is performed with a series of resynthesized three-syllable stimuli which differ by 14 locations of the pitch peak, two segmental bases used for resynthesis (declarative and interrogative) and two heights of the pitch peak (270 and 320Hz). The results of the experiment demonstrate that a shift in pitch peak alignment strongly affects listeners' perception of sentence type. The effects of pitch height and segmental base are also significant. Implications for Russian intonation system are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".