Intelligibility of older talkers on the NU6 test
Bibliographic record
Abstract
The stimuli used in laboratory studies on speech perception tend to be produced by younger talkers in ideal conditions. However, talker characteristics are much more variable in the real world, and include changes in speech due to normal aging and changes due to the environment. The first goal of this study was to investigate how the characteristics of older talkers affect listeners on a speech intelligibility test. The second goal was to investigate the extent to which older talkers can improve their intelligibility in a difficult communication situation. Eight healthy older talkers were selected who varied on their natural speech and voice characteristics. Talkers recorded the NU6 test in four conditions: speaking normally in quiet and in babble noise, and speaking as if to a listener with hearing loss in quiet and in babble noise. These recordings formed a new set of test materials that incorporated differences in talker characteristics as well as differences due to the communication environment. Young adult listeners with normal hearing were tested on these recordings in babble noise at -2 dB SNR. Listeners performed better on word recognition when talkers spoke in noise than when talkers spoke in quiet. Speech spoken as if to someone with hearing loss did not lead to better word recognition accuracy than speech spoken normally. Talkers varied on their overall intelligibility, with a range of 18 percentage points between the least and most intelligible talkers. Talkers were able to increase their intelligibility to the same extent when speaking in noise, regardless of their baseline intelligibility. Therefore, the characteristics of older talkers may dictate their baseline intelligibility, but they do not limit the extent to which talkers can increase intelligibility by changing their speech production.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".