Employing Deep Learning Model to Evaluate Speech Information in Acoustic Simulations of Auditory Implants

Acoustic simulations have played a prominent role in the development of speech processing and sound coding strategies for auditory neural implant devices. Traditionally evaluated using human subjects, acoustic simulations have been used to model the impact of implant signal processing as well as individual anatomy/physiology on speech perception. However, human subject testing is time-consuming, costly, and subject to individual variability. In this study, we propose a novel approach to perform simulations of auditory implants. Rather than using actual human participants, we utilized an advanced deep-learning speech recognition model to simulate the effects of some important signal processing as well as psychophysical/physiological factors on speech perception. Several simulation conditions were produced by varying number of spectral bands, input frequency range, envelope cut-off frequency, envelope dynamic range and envelope quantization. Our results demonstrate that the deep-learning model exhibits human-like robustness to simulation parameters in quiet and noise, closely resembling existing human subject results. This approach is not only significantly quicker and less expensive than traditional human studies, but it also eliminates individual human variables such as attention and learning. Our findings pave the way for efficient and accurate evaluation of auditory implant simulations, aiding the future development of auditory neural prosthesis technologies.