  Cochlear implant users struggle to understand speech in reverberant
environments. To restore speech perception, artifacts dominated by reverberant
reflections can be removed from the cochlear implant stimulus. Artifacts can be
identified and removed by applying a matrix of gain values, a technique
referred to as time-frequency masking. Gain values are determined by an oracle
algorithm that uses knowledge of the undistorted signal to minimize retention
of the signal components dominated by reverberant reflections. In practice,
gain values are estimated from the distorted signal, with the oracle algorithm
providing the estimation objective. Different oracle techniques exist for
determining gain values, and each technique must be parameterized to set the
amount of signal retention. This work assesses which oracle masking strategies
and parameterizations lead to the best improvements in speech intelligibility
for cochlear implant users in reverberant conditions using online speech
intelligibility testing of normal-hearing individuals with vocoding.
