Ultrasound speech training for Japanese adults learning English as a second language
Bibliographic record
Abstract
Japanese adults learning English as a second language often have difficulty perceiving and producing English /l/ and /ɹ/ due to specific acoustic and articulatory characteristics of these speech sounds and their absence in Japanese phonology. The current study investigated the effectiveness of using two-dimensional tongue ultrasound to teach pronunciation of these sounds to six adult native Japanese speakers. Each participant had four 45-minute training sessions over a two-week period where visual feedback from ultrasound was used to support the teaching of lingual configurations for /l/ and /ɹ/ in a variety of vowel contexts and word positions. Speech samples from participants were taken prior to training and at a two-week follow-up session. All participants were rated by expert listeners as having more accurate productions of /l/ and /ɹ/ post-training, with the most accuracy seen in word-initial clusters and as word-initial segments. The lateral /l/ showed greater improvement than /ɹ/. Acoustic and visual analyses revealed frequencies and components of tongue positioning closer to native English speaker production in words perceived to be greatly improved between pre- and post-training productions. The effect of training on perception was exploratory and did not yield analyzable results. All participants gave very positive feedback regarding the use of ultrasound for speech training, as determined by a participant questionnaire. The results suggest that incorporating lingual ultrasound in speech training can be beneficial for Japanese adults learning English liquids.
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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) | 0.000 | 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) | 0.002 | 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".