Laboratory assessment of daily-life speech understanding
Bibliographic record
Abstract
Current laboratory tests of speech understanding that are in common use in audiology do not incorporate important elements of daily listening that engage the cognitive elements of listening (e.g., attention, working memory). Given that cognitive processing takes on a particularly important role in sub-optimal listening scenarios, it is not surprising that traditional speech tests have had limited success in accounting for the complexities of daily listening caused by complex acoustic environments, listening goals that may change from moment to moment, hearing impairment, distortions caused by signal processing, and the auditory processing effects of aging. With the goal of developing better tools for assessing the impact of hearing impairment and sensory and cognitive interventions (hearing technology, auditory and cognitive training) on speech communication, we are developing a new speech test and measurement paradigm that incorporates important elements of daily listening that engage cognitive aspects of auditory processing. The new test focuses on understanding the meaning of speech rather than just hearing and reporting the phonetic elements of speech. It also incorporates important demands of daily listening such as the need for listeners to operate within the context of a continuous flow of speech information and with the presence of competing speech. This presentation will review how the test is constructed to achieve these elements of listening, and what impact these elements have on performance when compared to traditional tests of speech reception. Such research should lead to tests that are better than traditional tests at predicting the effects of treatments on real-life experiences of listening-instrument users. As such, they may form a part of a toolkit that clinicians deploy for determining intervention plans for their patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".