  Visual-to-auditory sensory substitution devices can assist the blind in
sensing the visual environment by translating the visual information into a
sound pattern. To improve the translation quality, the task performances of the
blind are usually employed to evaluate different encoding schemes. In contrast
to the toilsome human-based assessment, we argue that machine model can be also
developed for evaluation, and more efficient. To this end, we firstly propose
two distinct cross-modal perception model w.r.t. the late-blind and
congenitally-blind cases, which aim to generate concrete visual contents based
on the translated sound. To validate the functionality of proposed models, two
novel optimization strategies w.r.t. the primary encoding scheme are presented.
Further, we conduct sets of human-based experiments to evaluate and compare
them with the conducted machine-based assessments in the cross-modal generation
task. Their highly consistent results w.r.t. different encoding schemes
indicate that using machine model to accelerate optimization evaluation and
reduce experimental cost is feasible to some extent, which could dramatically
promote the upgrading of encoding scheme then help the blind to improve their
visual perception ability.
