Temporal and spectral characteristics of conversational versus read fricatives in American English
Bibliographic record
Abstract
The present study compares the production of fricatives in conversational versus read speech in American English. The goal is to examine which parameters contribute to the identification of fricatives across the two speech styles. The study surveys over 162 000 fricative tokens from the Buckeye Corpus [Pitt, Johnson, Hume, Kiesling, and Raymond (2005). Speech Commun. 45, 89-95] and the TIMIT Corpus [Zue and Seneff (1996). Recent Research towards Advanced Man-Machine Interface through Spoken Language (Elsevier, Amsterdam, the Netherlands), pp. 515-525]. A total of 18 different temporal and spectral measures are tested, including segment duration, preceding and following phone duration, spectral moments (at onset, midpoint, and/or offset), spectral peak frequency, etc. Results show that segment duration and midpoint spectral moments make the most prominent contribution to the categorization of fricatives for both speech styles. Spectral measures are more important for conversational speech, whereas duration plays a greater role for read speech. At the same time, the magnitude of the differences across speech styles is often low and many of the observed effects may be attributable to methodological differences across the corpora. Results may indicate that reduction of fricatives in conversational speech is more limited compared to the reduction of other types of speech sounds, such as plosives.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".