  This study introduces a biologically-inspired model designed to examine the
role of coincidence detection cells in speech segregation tasks. The model
consists of three stages: a time-domain cochlear model that generates
instantaneous rates of auditory nerve fibers, coincidence detection cells that
amplify neural activity synchronously with speech presence, and an optimal
spectro-temporal speech presence estimator. A comparative analysis between
speech estimation based on the firing rates of auditory nerve fibers and those
of coincidence detection cells indicates that the neural representation of
coincidence cells significantly reduces noise components, resulting in a more
distinguishable representation of speech in noise. The proposed framework
demonstrates the potential of brainstem nuclei processing in enhancing auditory
skills. Moreover, this approach can be further tested in other sensory systems
in general and within the auditory system in particular.
