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Record W6945852810 · doi:10.25949/21101362

The test of speech sound perception in noise (ToSSPiN) - effect of first language, spatial separation and reverberation on speech sound identification

2021· dissertation· en· W6945852810 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMacquarie University · 2021
Typedissertation
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsReverberationActive listeningAnechoic chamberNoise (video)PerceptionSpeech perceptionBackground noiseIdentification (biology)

Abstract

fetched live from OpenAlex

<strong>Aims: </strong>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. <strong>Design: </strong>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 ±90<sup>o</sup>, ±67.5<sup>o</sup> and ±45<sup>o</sup> 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. <strong>Samples: </strong>SRTs were collected in young adult native Australian-English speakers (<em>n </em>= 24), native Canadian-English speakers (<em>n </em>= 25) or non-native English speakers (<em>n </em>= 34). <strong>Results: </strong>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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.278
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it