  During speech perception, a listener's electroencephalogram (EEG) reflects
acoustic-level processing as well as higher-level cognitive factors such as
speech comprehension and attention. However, decoding speech from EEG
recordings is challenging due to the low signal-to-noise ratios of EEG signals.
We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding'
Signal Processing Grand Challenge. A simple ensembling method is shown to
considerably improve upon the baseline decoder performance. Even higher
classification rates are achieved by jointly decoding the speech-evoked
frequency-following response and responses to the temporal envelope of speech,
as well as by fine-tuning the decoders to individual subjects. Our results
could have applications in the diagnosis of hearing disorders or in cognitively
steered hearing aids.
