Advancements in phonetics in the 21st century: Infant speech development
Bibliographic record
Abstract
Infant speech perception emerged as a field late in the 20th century. Early work focused on defining the initial state, and documenting the timecourse of changes in speech perception over the first year of life. At the turn of the century, attention shifted from studying when children became attuned to their native language, to asking how children achieved this transformation. Statistical learning became the dominant mechanism to explain language development. But, as researchers pushed the bounds of statistical learning, different questions took center stage: given the complexity of spoken language, how do infants determine which regularities to track? And are the patterns infants track influenced by their unique language learning environment? Inspired by these questions, researchers have shifted to studying acquisition across more diverse contexts, and to using dense corpora and big data approaches to examine how individual differences in children’s input relate to speech perception in the lab. In this paper, we first review this progression, summarizing how the field has arrived at the current state of the art. We then argue that the time is ripe for the development of new theoretical approaches, and sketch out the loose contours of SLED, a new 21st-century proposal that emphasizes the role of sociophonetic variation and the richness of the speech signal in early development. With advanced tools in hand and data from a wide variety of learning contexts increasingly available, we are excited to see how the field will evolve over the next 25 years.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".