Expectation of speech style improves audio-visual perception of English vowels
Bibliographic record
Abstract
• Prior knowledge of speech style improved accuracy in identifying style and words. • Congruent style prompts improve accuracy by aligning with high-level expectations. • Clear speech enhances identification accuracy due to signal-internal modifications. • High-level information (such as speech style) is embodied in the signal. Speech perception is influenced by both signal-internal properties and signal-independent knowledge, including communicative expectations. This study investigates how these two factors interact, focusing on the role of speech style expectations. Specifically, we examine how prior knowledge about speech style (clear versus plain speech) affects word identification and speech style judgment. Native English perceivers were presented with English words containing tense versus lax vowels in either clear or plain speech, with trial conditions manipulating whether style prompts (presented immediately prior to the target word) were congruent or incongruent with the actual speech style. The stimuli were also presented in three input modalities: auditory (speaker voice), visual (speaker face), and audio-visual. Results show that prior knowledge of speech style improved accuracy in identifying style after the session when style information in the prompt and target word was consistent, particularly in auditory and audio-visual modalities. Additionally, as expected, clear speech enhanced word intelligibility compared to plain speech, with benefits more evident for tense vowels and in auditory and audio-visual contexts. These results demonstrate that congruent style prompts improve style identification accuracy by aligning with high-level expectations, while clear speech enhances word identification accuracy due to signal-internal modifications. Overall, the current findings suggest an interplay of processing sources of information which are both signal-driven and signal-independent, and that high-level signal-complementary information such as speech style is not separate from, but is embodied in, the signal.
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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 | 0.000 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".