Measuring Speech Intelligibility and Hearing-Aid Benefit Using Everyday Conversational Sentences in Real-World Environments

FRONTIERS IN NEUROSCIENCE(2022)

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摘要
Laboratory and clinical-based assessments of speech intelligibility must evolve to better predict real-world speech intelligibility. One way of approaching this goal is to develop speech intelligibility tasks that are more representative of everyday speech communication outside the laboratory. Here, we evaluate speech intelligibility using both a standard sentence recall task based on clear, read speech (BKB sentences), and a sentence recall task consisting of spontaneously produced speech excised from conversations which took place in realistic background noises (ECO-SiN sentences). The sentences were embedded at natural speaking levels in six realistic background noises that differed in their overall level, which resulted in a range of fixed signal-to-noise ratios. Ten young, normal hearing participants took part in the study, along with 20 older participants with a range of levels of hearing loss who were tested with and without hearing-aid amplification. We found that scores were driven by hearing loss and the characteristics of the background noise, as expected, but also strongly by the speech materials. Scores obtained with the more realistic sentences were generally lower than those obtained with the standard sentences, which reduced ceiling effects for the majority of environments/listeners (but introduced floor effects in some cases). Because ceiling and floor effects limit the potential for observing changes in performance, benefits of amplification were highly dependent on the speech materials for a given background noise and participant group. Overall, the more realistic speech task offered a better dynamic range for capturing individual performance and hearing-aid benefit across the range of real-world environments we examined.
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关键词
speech intelligibility,hearing aid benefit,realistic speech,clinical assessment development,speech in noise,ECO-SiN
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