Using automatic alignment on child speech: Directions for improvement
Bibliographic record
Abstract
Phonetic analysis is labor intensive, limiting the amount of data that can be considered. Automated techniques (e.g., forced alignment based on Automatic Speech Recognition, ASR) have recently emerged allowing for larger-scale analysis. While forced alignment can be accurate for adult speech (e.g., Yuan & Liberman, 2009), ASR techniques remain a challenge for child speech (Benzeghiba et al., 2007). We used a trainable forced aligner (Gorman et al., 2011) to examine the effect of four factors on alignment accuracy with child speech: (1) Datasets CHILDES (McWhinney, 2000):—Spontaneous speech (single child)—Picture naming (multiple children, Paidologos data); (2) Phonetic Transcription—Manual—Automatic—CMU dictionary (Weide, 1998); (3) Training data—Adult lab data—one dataset of child data—All child data—Child & adult lab data; (4) Segment—voiceless stops—voiceless sibilants—vowels Automatically generated alignments were compared to hand segmentations. While there were limits on accuracy, in general, better results were obtained with (1) picture naming, (2) manual phonetic transcription, (3) training data including child speech, and (4) voiceless stops. These four factors increase the utility of analyzing children’s speech production using forced alignment, potentially allowing researchers to conduct larger-scale studies that would not otherwise be feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.097 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".