A neural-based vocoder implementation for evaluating cochlear implant coding strategies

Most simulations of cochlear implant (CI) coding strategies rely on standard vocoders that are based on purely signal processing techniques. However, these models neither account for various biophysical phenomena, such as neural stochasticity and refractoriness, nor for effects of electrical stimulation, such as spectral smearing as a function of stimulus intensity. In this paper, a neural model that accounts for stochastic firing, parasitic spread of excitation across neuron populations, and neuronal refractoriness, was developed and augmented as a preprocessing stage for a standard 22-channel noise-band vocoder. This model was used to subjectively and objectively assess consonant discrimination in commercial and experimental coding strategies. Stimuli consisting of consonant-vowel (CV) and vowel-consonant-vowel (VCV) tokens were processed by either the Advanced Combination Encoder (ACE) or the Excitability Controlled Coding (ECC) strategies, and later resynthesized to audio using the aforementioned vocoder model. Baseline performance was measured using unprocessed versions of the speech tokens. Behavioural responses were collected from seven normal hearing (NH) volunteers, while EEG data were recorded from five NH participants. Psychophysical results indicate that while there may be a difference in consonant perception between the two tested coding strategies, mismatch negativity (MMN) waveforms do not show any marked trends in CV or VCV contrast discrimination.