Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users
Year: 2023
Type: article
Abstract: In the area of speech processing, human speaker identification under naturalistic environments is a challenging task, especially for hearing-impaired individuals with cochlear implants (CIs) or hearing aids (HAs). Motivated by the fact that electrodograms reflect direct CI stimulation of input audio, this study proposes a speaker identification (ID) investigation using two-dimensional electrodograms constructed from the responses of a CI auditory system to emulate CI speaker ID capabilities. Features are extracted from electrodograms through an identity vector (i-vector) framework to train and generate identity models for each speaker using a Gaussian mixture model-universal background model followed by probabilistic linear discriminant analysis. To validate the proposed system, perceptual speaker ID for 20 normal hearing (NH) and seven CI listeners was evaluated with a total of 41 different speakers and compared with the scores from the proposed system. A one-way analysis of variance showed that the proposed system can reliably predict the speaker ID capability of CI (F[1,10] = 0.18, p = 0.68) and NH (F[1,20] = 0, p = 0.98) listeners in naturalistic environments. The impact of speaker familiarity is also addressed, and the results show a reduced performance for speaker recognition by CI subjects using their CI processor, highlighting limitations of current speech processing strategies used in CIs/HAs.
DOI: 10.1121/10.0017216
Authors: Nursadul Mamun, Ria Ghosh, John H. L. Hansen
Institution: The University of Texas at Dallas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4321615128