Learning Prosodic Focus from Continuous Speech Input:A Neural Network Exploration
Bibliographic record
Abstract
This study uses connectionist modeling to explore whether and how infants might learn prosodic focus directly from continuous speech input. Focus is a communicative function that serves to put emphasis on a particular part of an utterance, and it is mainly encoded by pitch variations. The acquisition of focus entails two major difficulties. The first is that focus-related pitch patterns are confounded by other linguistic functions that also use pitch for their encoding, such as lexical tone in a tone language. Second, speakers have different pitch ranges, which further confounds the focus related pitch patterns. In three simulations using self-organizing neural networks, we explored how focus may be learned from continuous acoustic signals in Mandarin that were produced with co-occurring lexical tones and by multiple speakers. We used sentence-sized F0 contours as well as their velocity profiles (D1) as training input. Results show that both F0 and D1 contours provide information for focus learning, but only the D1-trained network adequately handled the variability introduced by cross-gender differences. The recognition rate was analogous to human performance. Implications of these findings for theories of language acquisition and adult speech perception are discussed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".