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Speaker recognition is a biometric modality that uses underlying speechinformation to determine the identity of the speaker. Speaker Identification(SID) under noisy conditions is one of the challenging topics in the field ofspeech processing, specifically when it comes to individuals with cochlearimplants (CI). This study analyzes and quantifies the ability of CI-users toperform speaker identification based on direct electric auditory stimuli. CIusers employ a limited number of frequency bands (8 to 22) and use electrodesto directly stimulate the Basilar Membrane/Cochlear in order to recognize thespeech signal. The sparsity of electric stimulation within the CI frequencyrange is a prime reason for loss in human speech recognition, as well as SIDperformance. Therefore, it is assumed that CI-users might be unable torecognize and distinguish a speaker given dependent information such as formantfrequencies, pitch etc. which are lost to un-simulated electrodes. To quantifythis assumption, the input speech signal is processed using a CI AdvancedCombined Encoder (ACE) signal processing strategy to construct the CI auditoryelectrodogram. The proposed study uses 50 speakers from each of three differentdatabases for training the system using two different classifiers under quiet,and tested under both quiet and noisy conditions. The objective result showsthat, the CI users can effectively identify a limited number of speakers.However, their performance decreases when more speakers are added in thesystem, as well as when noisy conditions are introduced. This information couldtherefore be used for improving CI-user signal processing techniques to improvehuman SID.

Quantifying Cochlear Implant Users' Ability for Speaker Identification using CI Auditory Stimuli