Abstract
The relation of the individual speech-in-noise performance differences in cochlear implant (CI) users to underlying physiological factors is currently poorly understood. This study approached this research question by a step-wise individualization of a computer model of speech intelligibility mimicking the details of CI signal processing and some details of the physiology present in CI users. Two factors, the electrical field spatial spread and internal noise (as a coarse model of the individual cognitive performance) were incorporated. Internal representations of speech-in-noise mixtures calculated by the model were classified using an automatic speech recognizer backend employing Hidden Markov Models with a Gaussian probability distribution. One-dimensional electric field spatial spread functions were inferred from electrical field imaging data of 14 CI users. Simplified assumptions of homogenously distributed auditory nerve fibers along the cochlear array and equal distance between electrode array and nerve tissue were assumed in the model. Internal noise, whose standard deviation was adjusted based on either anamnesis data, or text-reception-threshold data, or a combination thereof, was applied to the internal representations before classification. A systematic model evaluation showed that predicted speech-reception-thresholds (SRTs) in stationary noise improved (decreased) with decreasing internal noise standard deviation and with narrower electric field spatial spreads. The model version that was individualized to actual listeners using internal noise alone (containing average spatial spread) showed significant correlations to measured SRTs, reflecting the high correlation of the text-reception threshold data with SRTs. However, neither individualization to spatial spread functions alone, nor a combined individualization based on spatial spread functions and internal noise standard deviation did produce significant correlations with measured SRTs.
Highlights
Cochlear implant (CI) users experience greater difficulty than normal-hearing (NH) listeners to understand speech when background noise is present
Experiment 3 incorporates the individual factors into the physiologically-inspired CI model and compares the model predictions with the actual speech performance measured in each CI user
This study systematically evaluated a nonlinear model of CI user’s speech-in-noise performance with respect to the model-inherent factors electric field spatial spread and internal noise
Summary
Cochlear implant (CI) users experience greater difficulty than normal-hearing (NH) listeners to understand speech when background noise is present. In addition to this general problem, speech-in-noise performance varies considerably across CI users (e.g., [1]). Subjective (perceptual) measures include place pitch discrimination [6], spatial tuning curves [7], and electrode discrimination [8] These subjective measures characterize spectral resolution frequency-whereas other subjective measures such as spectral ripple discrimination or detection [9] and spectral modulation thresholds [10] usually employ broadband stimuli with variable spectral contrast, which are more similar to speech
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
