  The emerging field of neural speech recognition (NSR) using
electrocorticography has recently attracted remarkable research interest for
studying how human brains recognize speech in quiet and noisy surroundings. In
this study, we demonstrate the utility of NSR systems to objectively prove the
ability of human beings to attend to a single speech source while suppressing
the interfering signals in a simulated cocktail party scenario. The
experimental results show that the relative degradation of the NSR system
performance when tested in a mixed-source scenario is significantly lower than
that of automatic speech recognition (ASR). In this paper, we have
significantly enhanced the performance of our recently published framework by
using manual alignments for initialization instead of the flat start technique.
We have also improved the NSR system performance by accounting for the possible
transcription mismatch between the acoustic and neural signals.
