Representations of speech signals recorded through a dynamic periphery inspired by horseshoe bat biosonar

Horseshoe bats have to navigate through complex environments such as dense forests and structure-rich vegetation relying on input from their highly sophisticated biosonar systems. One of the key components of these bats' ability to obtain high-quality acoustic information is to alter the shape of their outer ears rapidly. In prior work, the authors have shown that by mimicking the horseshoe bat rapid ear movements, a bat-inspired robotic dynamic periphery for recording speech signals could enhance speech recognition for limited dataset and also provide estimates for the speaker's direction along with speech recognition. In our current study, we continued to investigate how speech datasets processed by the dynamic periphery may be enhanced compared to a reference by extracting acoustical features through Mel frequency cepstral coefficient (MFCC) transform, Lyon's cochlear bandpass filters, and a neural spike representation, respectively. This study aims to characterize the detailed acoustical differences and quantify the improved speaker intelligence with noise robustness through the dynamic periphery. The ultimate goal of this research is to identify a signal representation that is well suited to capitalize on the time-variant properties of the biomimetic recording periphery and make the dynamic information-bearing features accessible for the classification stages.