Phonetic variability of nasals and voiced stops in Japanese
Bibliographic record
Abstract
We investigated the phonetic variability of nasals and voiced stops in a large-scale Japanese speech corpus. We then analyzed the types of variation across speech styles. In particular, we examined the instances where target segments are deleted or realized as different phonemes. We identified 285 lexical entries displaying variability of target segments in the Corpus of Spontaneous Japanese (Maekawa, 2003). In line with the findings of Arai (1999), we observed a few variants of voiced stops, such as /d/ becoming [n] in /doɴna/ “what” and /g/ being deleted in /daigaku/ “university.” We also found /b/ turning into [m], such as /boku wa/ “I am” becoming [mo kaː]. For nasals, we observed /zeɴiɴ/ “all the people” and /geɴiɴ/ “reason” becoming [zẽːɪɴ] and [gẽːɪɴ], in which the first /ɴ/ was deleted and the preceding /e/ was nasalized and lengthened. Our findings suggest that the extent to which speakers produce phonetic variants of target segments is likely specified lexically more than stylistically because we find instances of [zẽːɪɴ] and [gẽːɪɴ] produced across multiple speech styles. Further, we find more than 90% of the instances of /geɴiɴ/ were pronounced as [gẽːɪɴ] and 80% of /zeɴiɴ/ were pronounced as [zẽːɪɴ].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".