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

Vocoder simulations have played a crucial role in the development of sound coding and speech processing techniques for auditory implant devices. Vocoders have been extensively used to model the effects of implant signal processing as well as individual anatomy and physiology on speech perception of implant users. Traditionally, such simulations have been conducted on human subjects, which can be time-consuming and costly. In addition, perception of vocoded speech varies significantly across individual subjects, and can be significantly affected by small amounts of familiarization or exposure to vocoded sounds. In this study, we propose a novel method that differs from traditional vocoder studies. Rather than using actual human participants, we use a speech recognition model to examine the influence of vocoder-simulated cochlear implant processing on speech perception. We used the OpenAI Whisper, a recently developed advanced open-source deep learning speech recognition model. The Whisper model's performance was evaluated on vocoded words and sentences in both quiet and noisy conditions with respect to several vocoder parameters such as number of spectral bands, input frequency range, envelope cut-off frequency, envelope dynamic range, and number of discriminable envelope steps. Our results indicate that the Whisper model exhibited human-like robustness to vocoder simulations, with performance closely mirroring that of human subjects in response to modifications in vocoder parameters. Furthermore, this proposed method has the advantage of being far less expensive and quicker than traditional human studies, while also being free from inter-individual variability in learning abilities, cognitive factors, and attentional states. Our study demonstrates the potential of employing advanced deep learning models of speech recognition in auditory prosthesis research.