Assessment of High-variability Speech Recognition in Adult Cochlear Implant Users using PRESTO

Background: Speech recognition in adult cochlear implant (CI) users is typically assessed using sentence materials with low talker variability. Little is known about the effects of talker variability on speech recognition in adult CI users, the factors underlying individual differences in speech recognition with high talker variability, or how sentence materials with high talker variability could be utilized clinically. Purpose: To examine the effects of talker variability on sentence recognition in adult CI users, using sentences from the Perceptually Robust English Sentence Test Open-Set (PRESTO), and to examine the relation between working memory capacity and high-variability speech recognition. Research Design: Postlingually deafened adult CI users and normal-hearing (NH) listeners under CI simulation completed sentence recognition tests that contained varying levels of talker variability, including HINT (low-variability), AzBio (moderate-variability), and PRESTO sentences (high-variability). The tasks were completed in both quiet and multi-talker babble (MTB). For the adult CI users only, the relation between sentence recognition accuracy and working memory capacity was assessed. Study Sample: Twenty postlingually deafened adult CI users and 35 NH adults under 8-channel acoustic noise-vocoder simulations of CI hearing. Results: In both CI and NH groups, performance decreased as a function of increased talker variability, with the best scores obtained on HINT (low-variability), then AzBio (moderate-variability), followed by PRESTO (high-variability) in quiet. In MTB, performance was significantly lower on PRESTO sentences, compared to HINT and AzBio sentences, which were not significantly different. Working memory capacity in the CI users was related to sentence recognition accuracy across all materials and conditions. Conclusions: Findings from the current study suggest that the increased talker variability in the PRESTO sentence materials has a detrimental effect on speech recognition in both adult CI users and NH listeners under CI simulation, particularly when speech is further degraded by MTB. For adult CI users, working memory capacity contributes to speech recognition abilities. Sentence recognition testing with high-variability, multi-talker materials, as in PRESTO, provides robust assessment of speech recognition abilities for research and clinical application, generating a wide range of scores for evaluating individual differences without ceiling effects when compared to conventional low-variability sentences.

Publikationsverlauf

Eingereicht: 14. Mai 2023

Angenommen nach Revision: 20. September 2023

Accepted Manuscript online:
25. September 2023