Speech-in-noise research: From classrooms to Canadian Armed Forces operational environments
Bibliographic record
Abstract
The importance of speech intelligibility in learning and occupational environments is evidenced by the abundance of research in room acoustics and auditory communication. In addition to environmental factors such as background noise and reverberation, individual factors including the presence of hearing loss, wearing of hearing aids and hearing protection devices (HPDs), and language proficiency must be considered. Previous work in these areas has provided a benchmark for the study of communication in complex, high noise environments. For Canadian Armed Forces (CAF) members, the high noise levels inside aircraft, armoured vehicles and sea vessels demand the use of HPDs and integrated radio communication systems. This presentation will summarize our research on speech understanding in noise and multi-talker environments using different communication headsets, simulated hearing loss and language proficiency. We will focus on our recent studies of communication between native (L1) and non-native (L2) English speakers, which is applicable to the CAF due to the relatively high percentage of L2 membership. L2 speakers obtained lower scores on speech-in-noise tests than L1 speakers for both radioed and face-to-face speech, despite having a high level of language proficiency. Preliminary results of an investigation of cognitive load for L2 listeners will also be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".