T01 A method to convert between Speech Recognition Thresholds (SRT) and percentage-correct scores for speech-in-noise tests
Different approaches have been taken in the development of speech-in-noise tests to quantify speech recognition abilities in noise. Speech-in-noise tests most often use either fixed-SNR procedures to measure the percentage of correctly recognized speech items at a fixed signal-to-noise ratio (SNR) or use adaptive procedures to measure the SNR corresponding to 50% correct (i.e., the speech recognition threshold, SRT). Both procedures have advantages and disadvantages. Unfortunately, a direct, simple, comparison of these measures is not possible yet.
It will be demonstrated that these measures can be converted by using a relatively easy analytical method, 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 SII model and a representative set of 60 audiograms (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.