Vowel normalization and the perception of speaker changes: An exploration of the contextual tuning hypothesis
Bibliographic record
Abstract
Many experiments have reported a perceptual advantage for vowels presented in blocked-versus mixed-voice conditions. Nusbaum and colleagues [Nusbaum and Morin (1992). in Speech Perception, Speech Production, and Linguistic Structure, edited by Y. Tohkura, Y. Sagisaka, and E. Vatikiotis-Bateson (OHM, Tokyo), pp. 113-134; Magnuson and Nusbaum (2007). J. Exp. Psychol. Hum. Percept. Perform. 33(2), 391-409] present results which suggest that the size of this advantage may be related to the facility with which listeners can detect speaker changes, so that combinations of less similar voices can result in better performance than combinations of more similar voices. To test this, a series of synthetic voices (differing in their source characteristics and/or formant-spaces) was used in a speeded-monitoring task. Vowels were presented in blocks made up of tokens from one or two synthetic voices. Results indicate that formant-space differences, in the absence of source differences between voices in a block, were unlikely to result in the perception of multiple voices, leading to lower accuracy and relatively faster reaction times. Source differences between voices in a block resulted in the perception of multiple voices, increased reaction times, and a decreased negative effect of formant-space differences between voices on identification accuracy. These results are consistent with a process in which the detection of speaker changes guides the appropriate or inappropriate use of extrinsic information in normalization.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".