Avoiding speaker variability in pronunciation verification of children's disordered speech
Bibliographic record
Abstract
This paper deals with the problematic of speaker variability in a task of pronunciation verification for the speech therapy of children and young adults in Computer-Aided Pronunciation Training (CAPT) tools. The baseline system evaluates two different score normalization techniques: Traditional Test normalization (T-norm), and a novel N-best based normalization that outperforms the first by normalizing to the log-likelihood score of the first alternative phoneme in an unconstrained N-best list. When performing speaker adaptation, the use of all the adaptation data from the speaker improves the performance measured in Equal Error Rate (EER) of these systems compared to the speaker independent systems; but this can be outperformed by more precise models that only adapt to the correctly pronounced phonetic units as labeled by a set of human experts. The best EER obtained in all experiments is 15.63% when using both elements: Score normalization and speaker adaptation. The possibility of automatizing a more precise adaptation without the human intervention is finally proposed and discussed.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".