Speech comprehension in the context of speaker changes: The importance of voice-feature continuity at the cocktail party
Bibliographic record
Abstract
In this talk, I will show how different groups of listeners use voice acoustics to enhance speech comprehension under adverse listening conditions, specifically when the auditory scene comprises a multitude of sounds heard at once. These “cocktail-party”-like situations pose a difficult conceptual problem: To comprehend target speech, listeners need to attend to the target voice while at the same time ignoring other irrelevant sounds. The cocktail-party problem is made considerably easier when all target sounds are spoken by the same talker. Previous work suggests that such benefits from voice continuity can be—in large part—attributed to two prominent voice features: Glottal-pulse rate (GPR) and vocal-tract length (VTL). GPR determines the fundamental frequency of a speech sound and is perceived as vocal pitch; VTL determines the spectral envelope of a speech sound and is perceived as an aspect of vocal timbre. Apart from being important voice identity cues, GPR and VTL have been shown to play a crucial role in cocktail-party listening. Here, I will present data from a series of experiments highlighting the importance of voice-feature continuity for speech comprehension at the cocktail party.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".