Perception modeling of native and foreign-accented Japanese speech based on prosodic features of pitch accent
Bibliographic record
Abstract
This study investigates the influence acoustic measures of pitch accent have on L1 and Australian English (AusE) L2 Japanese speech perception, expanding Tsurutani (2010) and Ishihara, Tsurutani, and Tsukada (2011), and motivated by Munro and Derwing (2001), which studies the role of speaking rate on judgments of L2 speech. We establish native and advanced AusE listeners of Japanese differ in their judgments of foreign accent in terms of accentedness and comprehensibility [Munro and Derwing (1995, 1999)] through a listening task. Selected acoustic measures of pitch accent from the speech stimuli, which displayed significant variance across listener groups—delta-pitch, max and mean max delta-intensity, and duration per mora—are correlated with L1 and L2 listener data. Testing for a relationship between each of the acoustic measures and listener judgments, the regression analyses show a considerable relationship between comprehensibility judgments and duration and intensity features, ranging from adjusted R2 = 14.3% to 24.6% across listeners, and indicating the degree of variance between judgments can be attributed to these acoustic measures. We can interpret that comprehensibility is linked to intensity and duration, which supports the authors’ prior findings that timing is considered more important than pitch in the detection of foreign-accented speech.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".