Investigating Emphatic Consonants in Foreign Accented Arabic
Bibliographic record
Abstract
This paper investigates the four emphatic consonants of Arabic from the point of view of automatic speech recognition. Comparisons of the recognition error rates for these phonemes and for their non-emphatic counterparts are analyzed in five experiments that involve different combinations of native and non-native Arabic speakers. In addition, the target consonants are described in time-frequency domain analyses. All experiments used the Hidden Markov Model toolkit (HTK) and the Language Data Consortium (LDC) WestPoint Modern Standard Arabic (MSA) database. Results confirm that emphatic consonants are a major source of difficulty for ASR. While the recognition rate for certain emphatic consonants such as /D/ can drop below 15% when uttered by non-native speakers, there are advantages to including non-native speakers in ASR. Regional differences in the pronunciation of MSA by native Arabic speakers require the attention of Arabic ASR research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| 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".