Changes in the acoustic characteristics of speech in the later years of life
Bibliographic record
Abstract
Acoustic and perceptual research has shown that an individual's vocal characteristics change over time [e.g., Harnsberger et al., J. Voice 22(1), 2334–2350 (2008)]. Most previous studies have investigated speech changes over time using cross-sectional data. The present study is a later-life longitudinal investigation of three speakers using publicly available archives of speeches given to large audiences on a semi-regular basis (generally with a couple of years between each instance). The group of speeches was given during the last 30–50 years of each speakers' life. From each speech, 5-minutesamples (recordings and transcripts) were force-aligned to identify word and phoneme boundaries. Acoustic characteristics of the speech were extracted from the speech signal using Praat. In the present analysis, we investigate changes in the vowel space, pitch, word duration, segment duration, and speech rate. These acoustic characteristics are modeled using Generalized Additive Models [Hastie and Tibshirani, Generalized Additive Models (1990)] to allow for non-linear changes over time. The results are discussed in terms of vocal changes over the lifespan in the speakers' later-years. We find that not all of the effects are as expected and that the effects are idiosyncratic and dynamic over the lifespan.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".