  Auditory attention decoding (AAD) is a technique used to identify and amplify
the talker that a listener is focused on in a noisy environment. This is done
by comparing the listener's brainwaves to a representation of all the sound
sources to find the closest match. The representation is typically the waveform
or spectrogram of the sounds. The effectiveness of these representations for
AAD is uncertain. In this study, we examined the use of self-supervised learned
speech representation in improving the accuracy and speed of AAD. We recorded
the brain activity of three subjects using invasive electrocorticography (ECoG)
as they listened to two conversations and focused on one. We used WavLM to
extract a latent representation of each talker and trained a spatiotemporal
filter to map brain activity to intermediate representations of speech. During
the evaluation, the reconstructed representation is compared to each speaker's
representation to determine the target speaker. Our results indicate that
speech representation from WavLM provides better decoding accuracy and speed
than the speech envelope and spectrogram. Our findings demonstrate the
advantages of self-supervised learned speech representation for auditory
attention decoding and pave the way for developing brain-controlled hearable
technologies.
