The test of speech sound perception in noise (ToSSPiN) - effect of first language, spatial separation and reverberation on speech sound identification
Bibliographic record
Abstract
Aims: The first aim of the study is to investigate the effect of native language on the ToSSPiN in Australian English, Canadian English, and non-native English-speaking people. The second aim is to investigate the differences in performance on the Test of Speech Sound Perception in Noise (ToSSPiN) in face-to-face and remote delivery modes. The final aim is to determine if each phoneme is equal in difficulty and adjust them so that, on average, each are identified 71% of the time at an identical signal-to-noise ratio. Design: ToSSPiN targets comprised consonant-vowel-consonant-vowel (CVCV) pseudowords (e.g. /tigu/). Distractors comprised CVCVCVCV pseudo-words. Stimuli were presented using an iPad and headphones. Participants were tested face-to-face at Macquarie University with a researcher recording their responses or remotely via Zoom with a testing partner recording the responses. Scoring occurred adaptively to establish a participant’s speech reception threshold (SRT) expressed as dB signal-to-noise ratio. The listening environment was simulated using reverberant and anechoic head-related transfer functions, creating ecologically valid acoustics. The listening environment also varied in whether the distractors were voiced by the same or different voices from the targets. In the baseline ToSSPiN conditions, the targets originated from 0o azimuth. The distractors originated from ±90o, ±67.5o and ±45o in the spatially separated conditions and 0o in the co-located condition. Reverberation impact (RI) was calculated as the SRT (in dB) in the anechoic condition minus the SRT (in dB) in the reverberant condition. Spatial advantage (SA) was calculated as the SRT (in dB) in the spatially separated condition minus the SRT (in dB) in the co-located condition. Samples: SRTs were collected in young adult native Australian-English speakers (n = 24), native Canadian-English speakers (n = 25) or non-native English speakers (n = 34). Results: No significant effects of language occurred for the baseline measures, RI or SA. A small but significant effect of delivery mode occurred for RI, but not for SA or baseline measures. Psychometric functions obtained for individual phonemes differed notably and phonemes required adjustments ranging from -2.0 dB for /t/ to +8.7 dB for /h/ to attain equal intelligibility.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".