用神经信号重建语音，模拟人工耳蜗听觉效果

We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. The system applies straightforward signal processing to neurogram representations, time-frequency binned outputs from auditory nerve fiber models. Crucially, the model architecture is modular, allowing for easy substitution or modification of the underlying auditory models. This flexibility eliminates the need for speech-coding-strategy-specific vocoder implementations when simulating auditory perception in cochlear implant (CI) users. It also allows direct comparisons between normal hearing (NH) and electrical hearing (EH) models, as demonstrated in this study. The vocoder preserves distinctive features of each model; for example, the NH model retains harmonic structure more faithfully than the EH model. We evaluated perceptual intelligibility in noise using an online Digits-in-Noise (DIN) test, where participants completed three test conditions: one with standard speech, and two with vocoded speech using the NH and EH models. Both the standard DIN test and the EH-vocoded groups were statistically equivalent to clinically reported data for NH and CI listeners. On average, the NH and EH vocoded groups increased SRT compared to the standard test by 2.4 dB and 7.1 dB, respectively. These findings show that, although some degradation occurs, the vocoder can reconstruct intelligible speech under both hearing models and accurately reflects the reduced speech-in-noise performance experienced by CI users.