The development of an Afrikaans test for sentence recognition thresholds in noise

Theunissen, Marianne; Hanekom, Johan J.; Swanepoel, De Wet

doi:10.3109/14992027.2010.532511

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…The Afrikaans lists were produced by a female audiologist whose voice had previously been evaluated for articulation, resonance and voice quality. This was the same speaker who produced the sentences for the Afrikaans test of sentence recognition thresholds in noise (Theunissen et al, 2011). The English lists were produced by a female audiologist who is a native speaker of South African English and speaks with an accent that is considered to be representative of the province of South Africa where the research was conducted (Gauteng).…”

Section: Methodsmentioning

confidence: 99%

Modernising speech audiometry: using a smartphone application to test word recognition

Zyl

Swanepoel

Myburgh

2018

International Journal of Audiology

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Section: Methodsmentioning

confidence: 99%

Modernising speech audiometry: using a smartphone application to test word recognition

Zyl

Swanepoel

Myburgh

2018

International Journal of Audiology

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“…First, one needs assurance that test scores are reliable from test to retest. Numerous researchers have demonstrated good test-retest reliability of sentence recognition materials in noise from one test session to a second (see, e.g., Bentler, 2000;Brown, Cameron, Martin, Watson, & Dillon, 2010;Dawson, Hersbach, & Swanson, 2013;Kuk et al, 2010;Pugh, Crandell, & Griffiths, 1998;Terband & Drullman, 2008;Theunissen, Hanekom, & Swanepoel, 2011). Repeated (i.e., more than two) measurements of sentence recognition may be problematic if performance is affected by learning in the absence of an underlying general improvement in speech perception (Yund & Woods, 2010).…”

mentioning

confidence: 99%

No Learning Effect Observed for Reception Thresholds for Sentences in Noise

Stuart

Butler

2014

Am J Audiol

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An analytical method to convert between speech recognition thresholds and percentage-correct scores for speech-in-noise tests

Smits

Sousa

Swanepoel

2021

The Journal of the Acoustical Society of America

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Speech-in-noise tests use fixed signal-to-noise ratio (SNR) procedures to measure the percentage of correctly recognized speech items at a fixed SNR or use adaptive procedures to measure the SNR corresponding to 50% correct (i.e., the speech recognition threshold, SRT). A direct comparison of these measures is not possible yet. The aim of the present study was to demonstrate that these measures can be converted when the speech-in-noise test meets specific criteria. Formulae to convert between SRT and percentage-correct were derived from basic concepts that underlie standard speech recognition models. Information about the audiogram is not being used in the proposed method. The method was validated by comparing the direct conversion by these formulae with the conversion using the more elaborate Speech Intelligibility Index model and a representative set of 60 audiograms (r = 0.993 and r = 0.994, respectively). Finally, the method was experimentally validated with the Afrikaans sentence-in-noise test (r = 0.866). The proposed formulae can be used when the speech-in-noise test uses steady-state masking noise that matches the spectrum of the speech. Because pure tone thresholds are not required for these calculations, the method is widely applicable.

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The development of an Afrikaans test for sentence recognition thresholds in noise

Cited by 7 publications

References 29 publications

Modernising speech audiometry: using a smartphone application to test word recognition

Modernising speech audiometry: using a smartphone application to test word recognition

No Learning Effect Observed for Reception Thresholds for Sentences in Noise

An analytical method to convert between speech recognition thresholds and percentage-correct scores for speech-in-noise tests

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