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 (r¼0.993 and
r¼0.994, respectively). Finally, the method was experimentally validated with the Afrikaans sentence-in-noise test
(r¼0.866). 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.
Overt attempts at self-correction of speech errors reflect conscious monitoring of speech output. The ability to monitor speech reveals something about the dynamics of motor control. Speakers with apraxia of speech (AOS) ...
The present study is the first to examine the effect of first versus second language (L1 versus L2) speech production on specific temporal parameters of speech in bilingual speakers with neurogenic speech disorders. Three ...
Van der Merwe, Anita; Le Roux, Mia(AOSIS OpenJournals, 2014-12-03)
The objective of this article is to create awareness amongst speech-language pathologists and
audiologists in South Africa regarding the difference between the sound systems of Germanic
languages and the sound systems ...