Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 31, Issue -, Pages 2326-2337Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2023.3274604
Keywords
Neural prosthetic; cochlear implant; aural rehabilitation; phoneme recognition; vocoder simulation
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In this study, a new acoustic vocoder model was used to simulate perception with cochlear implants (CIs), and it was hypothesized that comparable speech encoding could lead to similar perceptual patterns for CI and normal hearing (NH) listeners. The results showed that the same signal encoding implementations could lead to similar perceptual patterns simultaneously in multiple perception tasks, highlighting the importance of faithfully replicating all signal processing stages in sensory neuroprostheses.
Perception with electric neuroprostheses is sometimes expected to be simulated using properly designed physical stimuli. Here, we examined a new acoustic vocoder model for electric hearing with cochlear implants (CIs) and hypothesized that comparable speech encoding can lead to comparable perceptual patterns for CI and normal hearing (NH) listeners. Speech signals were encoded using FFT-based signal processing stages including band-pass filtering, temporal envelope extraction, maxima selection, and amplitude compression and quantization. These stages were specifically implemented in the same manner by an Advanced Combination Encoder (ACE) strategy in CI processors and Gaussian-enveloped Tones (GET) or Noise (GEN) vocoders for NH. Adaptive speech reception thresholds (SRTs) in noise were measured using four Mandarin sentence corpora. Initial consonant (11 monosyllables) and final vowel (20 monosyllables) recognition were also measured. Naive NH listeners were tested using vocoded speech with the proposed GET/GEN vocoders as well as conventional vocoders (controls). Experienced CI listeners were tested using their daily-used processors. Results showed that: 1) there was a significant training effect on GET vocoded speech perception; 2) the GEN vocoded scores (SRTs with four corpora and consonant and vowel recognition scores) as well as the phoneme-level confusion pattern matched with the CI scores better than controls. The findings suggest that the same signal encoding implementations may lead to similar perceptual patterns simultaneously in multiple perception tasks. This study highlights the importance of faithfully replicating all signal processing stages in the modeling of perceptual patterns in sensory neuroprostheses. This approach has the potential to enhance our understanding of CI perception and accelerate the engineering of prosthetic interventions. The GET/GEN MATLAB program is freely available athttps://github.com/BetterCI/GETVocoder.
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