Digging into sentence intelligibility: Interactions with noise
Bibliographic record
Abstract
Implicit in the design of many intelligibility and perception studies is the assumption that noise masks all sentence stimuli equivalently, at least when they are controlled for length and structure. For example, controlled and normed sentences, such as the IEEE Harvard set, are typically thought to be equally affected by masker-noise. However, previous research has established that spoken stimuli of different structures, lengths, or complexities, are differentially masked (e.g. nonsense versus real words, words with different usage frequencies), therefore it is possible that masking affects even controlled sentences differentially . Using the UAW speech intelligibility dataset we analyzed Levenshtein Distance values based on transcriptions from over 900 native listeners of English to over 604 sentences from the UWNU IEEE sentence corpus. Each sentence was presented in noise at three SNRs ( + 2, 0, −2 dB). We find that the sentences are not equally intelligible. Moreover, there is an interaction with SNR level and sentence. In other words, the intelligibility of the sentences is impacted differentially by masker-noise. The results of this study suggest that, as researchers using speech stimuli, we should recognize that there are many sentence level factors that may introduce variance or otherwise affect the outcomes of our studies.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".