Learning Prosodic Focus from Continuous Speech Input:A Neural Network Exploration
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it