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

On Neural Phone Recognition of Mixed-Source ECoG Signals