1. Diagnostic Accuracy of the AzBio Speech Recognition in Noise Test
- Author
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Vermiglio, Andrew J., Leclerc, Lauren, Thornton, Meagan, Osborne, Hannah, Bonilla, Elizabeth, and Fang, Xiangming
- Abstract
Purpose: The goal of this study was to determine the ability of the AzBio speech recognition in noise (SRN) test to distinguish between groups of participants with and without a self-reported SRN disorder and a self-reported signal-to-noise ratio (SNR) loss. Method: Fifty-four native English-speaking young adults with normal pure-tone thresholds ([less than or equal to] 25 dB HL, 0.25-6.0 kHz) participated. Individuals who reported hearing difficulty in a noisy restaurant (Reference Standard 1) were placed in the SRN disorder group. SNR loss groups were created based on the self-report of the ability to hear Hearing in Noise Test (HINT) sentences in steady-state speech-shaped noise, four-talker babble, and 20-talker babble in a controlled listening environment (Reference Standard 2). Participants with HINT thresholds poorer than or equal to the median were assigned to the SNR loss group. Results: The area under the curve from the receiver operating characteristics curves revealed that the AzBio test was not a significant predictor of an SRN disorder, or an SNR loss using the steady-state noise Reference Standard 2 condition. However, the AzBio was a significant predictor of an SNR loss using the four-talker babble and 20-talker babble Reference Standard 2 conditions (p < 0.05). The AzBio was a significant predictor of an SNR loss when using the average HINT thresholds across the three Reference Standard 2 masker conditions (area under the curve = 0.79, p = 0.001). Conclusions: The AzBio test was not a significant predictor of a self-reported SRN disorder or a self-reported SNR loss in steady-state noise. However, it was a significant predictor of a self-reported SNR loss in babble noise and the average across all noise conditions. A battery of reference standard tests with a range of maskers in a controlled listening environment is recommended for diagnostic accuracy evaluations of SRN tests.
- Published
- 2021
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