Cochlear implant speech intelligibility outcomes with structured and unstructured binary mask errors

It has been shown that intelligibility can be improved for cochlear implant (CI) recipients with theideal binary mask (IBM). In realistic scenarios where prior information is unavailable, however, theIBM must be estimated, and these estimations will inevitably contain errors. Although the effectsof both unstructured and structured binary mask errors have been investigated with normal-hearing(NH) listeners, they have not been investigated with CI recipients. This study assesses these effectswith CI recipients using masks that have been generated systematically with a statistical model.The results demonstrate that clustering of mask errors substantially decreases the tolerance oferrors, that incorrectly removing target-dominated regions can be as detrimental to intelligibility asincorrectly adding interferer-dominated regions, and that the individual tolerances of the differenttypes of errors can change when both are present. These trends follow those of NH listeners.However, analysis with a mixed effects model suggests that CI recipients tend to be less tolerantthan NH listeners to mask errors in most conditions, at least with respect to the testing methods ineach of the studies. This study clearly demonstrates that structure influences the tolerance of errorsand therefore should be considered when analyzing binary-masking algorithms.