Hypernasal Speech Is Perceived as More Monotonous than Typical Speech
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Anecdotal clinical reports have stated that hypernasal speech sounds monotonous. However, the relationship between the perception of intonation (i.e., the fundamental frequency variation across an utterance) and hypernasality (excessive nasal resonance during the production of non-nasal sounds) has not been investigated in research. We hypothesized that auditory-perceptual ratings of intonation would be significantly lower for more hypernasal stimuli. METHODS: One male and one female voice actor simulated 3 levels of intonation (monotone, normal, and exaggerated) at 4 different levels of hypernasality (normal, mild, moderate, and severe). Thirty participants listened to the simulations and rated the intonation on a visual analogue scale from 0 (monotone) to 100 (exaggerated). RESULTS: A mixed-effects ANOVA revealed main effects of intonation (F2 = 236.46, p < 0.001), and hypernasality (F3 = 159.89, p < 0.001), as well as an interaction between the two (F6 = 28.35, p < 0.001). Post hoc analyses found that speech was rated as more monotonous as hypernasality increased. Summary/Implications: The presence of hypernasality in speech can lead listeners to perceive speech as more monotonous. Instrumental measures should be used to corroborate auditory-perceptual evaluations of speech features like intonation.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".