Training listeners or preserving phase information improves the effect of perceived spatial separation on releasing spectrally degraded Chinese speech from information masking
Bibliographic record
Abstract
Physical or perceived spatial signal/masker separation unmasks speech more when maskers are informational than when energetic. However, it is unclear how beneficial the separations are to cochlear-implant listeners, because signal transductions applied in cochlear implant degrade signals spectrally, and spectrally degraded speech is more vulnerable to maskers. Here, spectrums of both target speech (nonsense sentence) and masker (steady speech-spectrum noise, speech modulated speech C-spectrum noise, or speech) were filtered into 15 frequency bands. For both target and masking speech, the center-frequency pure tone of each band was modulated by the extracted envelope from the band. The target speech was composed by the sum of the 8 odd-band tones, and the masker was either same-band (with the 8 odd-band tones) or different-band (with the 7 even-band tones). The results show that physical but not perceived spatial separation unmasked target speech in naive normal-hearing listeners. However, following pre-presentations of both degraded and normal correspondent speech to listeners for a period of time or the introduction of phase information into modulated tones, perceived spatial separation reduced the influence of different-band speech masking but not that of same-band speech masking. These results are useful for improving cochlear-implant programs at both behavioral and technical levels.
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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) | 0.004 | 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".