  The electroencephalogram (EEG) offers a non-invasive means by which a
listener's auditory system may be monitored during continuous speech
perception. Reliable auditory-EEG decoders could facilitate the objective
diagnosis of hearing disorders, or find applications in cognitively-steered
hearing aids. Previously, we developed decoders for the ICASSP Auditory EEG
Signal Processing Grand Challenge (SPGC). These decoders aimed to solve the
match-mismatch task: given a short temporal segment of EEG recordings, and two
candidate speech segments, the task is to identify which of the two speech
segments is temporally aligned, or matched, with the EEG segment. The decoders
made use of cortical responses to the speech envelope, as well as
speech-related frequency-following responses, to relate the EEG recordings to
the speech stimuli. Here we comprehensively document the methods by which the
decoders were developed. We extend our previous analysis by exploring the
association between speaker characteristics (pitch and sex) and classification
accuracy, and provide a full statistical analysis of the final performance of
the decoders as evaluated on a heldout portion of the dataset. Finally, the
generalisation capabilities of the decoders are characterised, by evaluating
them using an entirely different dataset which contains EEG recorded under a
variety of speech-listening conditions. The results show that the
match-mismatch decoders achieve accurate and robust classification accuracies,
and they can even serve as auditory attention decoders without additional
training.
