  Speaker recognition is a biometric modality that uses underlying speech
information to determine the identity of the speaker. Speaker Identification
(SID) under noisy conditions is one of the challenging topics in the field of
speech processing, specifically when it comes to individuals with cochlear
implants (CI). This study analyzes and quantifies the ability of CI-users to
perform speaker identification based on direct electric auditory stimuli. CI
users employ a limited number of frequency bands (8 to 22) and use electrodes
to directly stimulate the Basilar Membrane/Cochlear in order to recognize the
speech signal. The sparsity of electric stimulation within the CI frequency
range is a prime reason for loss in human speech recognition, as well as SID
performance. Therefore, it is assumed that CI-users might be unable to
recognize and distinguish a speaker given dependent information such as formant
frequencies, pitch etc. which are lost to un-simulated electrodes. To quantify
this assumption, the input speech signal is processed using a CI Advanced
Combined Encoder (ACE) signal processing strategy to construct the CI auditory
electrodogram. The proposed study uses 50 speakers from each of three different
databases for training the system using two different classifiers under quiet,
and tested under both quiet and noisy conditions. The objective result shows
that, the CI users can effectively identify a limited number of speakers.
However, their performance decreases when more speakers are added in the
system, as well as when noisy conditions are introduced. This information could
therefore be used for improving CI-user signal processing techniques to improve
human SID.
