Learning to categorize lexical tones in sentences with variable narrow focus: A neural network approach
Bibliographic record
Abstract
The surface F0 patterns of lexical tones contain variability from many different sources, which raises questions as to how infants can learn tones based on such highly variable input. A recent modeling study showed that the velocity profiles of F0 (i.e., first derivatives, or D1) enable naive learners to successfully categorize the four Mandarin tones despite cross-speaker and contextual variations [Gauthier, etal. ‘‘Recognising tones by tracking moments—How infants may develop tonal catagories from adult speech input,’’ in Proceedings ISCA workshop on plasticity in Speech Perception, edited by V. Dellwo (UCL Publications, London, UK, 2005) pp. 72–75]. The present study explores the robustness of D1 for categorizing Mandarin tones in sentences said with different focus conditions. As a contrastive communicative function, narrow focus within a multi-word utterance introduces extensive variability to F0, making tone learning a more challenging task. Using multi-speaker productions of utterances with systematically varied tones and focus [Xu, ‘‘Effects of tone and focus on the formation and alignment of contours. J. Phonet. 27, 55–105 (1999)], self-organizing neural networks were trained with both syllable-sized D1 and F0 as input, with no special treatment for different focus conditions. In the testing phase, novel tokens were tested for tonal categorization. The results revealed that D1 yielded overall excellent categorization, far superior than F0. Detailed analyses showed that with D1, performance of tonal recognition dropped only in post-focus regions. These findings indicate that successful tone learning can be achieved with velocity profiles as input despite variability introduced by focus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".