Recognition of Mandarin Chinese in noisy, reverberant environments
Bibliographic record
Abstract
Cochlear-implant users partially recover their speech intelligibility in quiet but not in a noisy, reverberant environment, particularly for those speaking tonal languages, for which semantic information is also expressed by pitch contour. To improve cochlear-implant algorithms for tonal-language users, we have investigated speech recognition in Mandarin-Chinese speaking listeners under adverse listening condition to address four issues related to perceptual fusion and informational masking. First, to what extent do Chinese speech and non-speech sounds differ with respect to the tendency of perceptual fusion (between direct and reflected waves)? Second, why does perceptual separation provide a smaller release from informational masking in Mandarin Chinese than in English? Third, can the use of the recently developed simulated phase-locking stimulation strategy (SPLS, which extracts both phase and amplitude-envelope information) improve speech perception in Mandarin-speaking cochlear-implant patients compared to the continuous interleaved sampling strategy (CIS) currently in use? Fourth, does the prior presentation of a sentence spoken in quiet, by the same person who immediately afterwards produces a masked target sentence, improve identification of the masked target (voice priming) only for tonal-language speaking listeners?
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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 | 0.002 |
| 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 | 0.001 |
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".