Cochlear implants (CIs) provide hearing for over 200,000 recipients worldwide {NIDCD, 2011 #637}. These devices successfully provide high levels of speech understanding in quiet listening conditions; however, more challenging conditions degrade speech comprehension for CI recipients to a much greater degree than for normal hearing listeners {Kokkinakis, 2011 #673;Nelson, 2003 #302}. CI listeners are especially affected by reverberant conditions with even a small level of reverberation degrading comprehension to a greater degree than a large amount of steady-state noise {Hazrati, 2012 #674}. Thus, a method that mitigates the effects of reverberation has the potential to greatly improve the quality of life for CI users. Previous attempts to solve the problem of speech in reverberation for cochlear implants have not been able to be implemented in real time. Our preliminary results suggest that successful mitigation of overlap masking can result in a substantial improvement in speech recognition even if self-masking is not mitigated and we have devised an approach that can be implemented in real time. In the proposed effort, we will first improve the classifier to detect reverberation based on our successful preliminary efforts. Next we will assess the mitigation algorithm, first in normal hearing listeners and then in listeners with cochlear implants. Finally, we will implement the algorithm in real time and again test it.
While cochlear implants (CIs) provide high levels of speech comprehension in quiet for over 200,000 profoundly deaf individuals worldwide, speech in quiet scenarios are rarely encountered outside of the home. The presence of noise or reverberation in the listening environment decreases speech comprehension for CI users much more rapidly than for normal-hearing listeners, and of these two conditions, reverberation has a greater negative impact. The proposed research aims to provide a robust reverberation mitigation algorithm for CI speech processors thereby improving the ability of CI users to interact in real-world listening environments.
